CN113875187B

CN113875187B - Prediction of use or adherence

Info

Publication number: CN113875187B
Application number: CN201980086957.3A
Authority: CN
Inventors: 内森·刘; 萨基娜·德·索萨; 奥列克桑德·格罗门科
Original assignee: Resmed Paris SAS
Current assignee: Resmed Paris SAS
Priority date: 2018-12-28
Filing date: 2019-12-23
Publication date: 2025-03-25
Anticipated expiration: 2039-12-23
Also published as: US20220076822A1; KR20210113622A; CN120199481A; AU2019417727A1; WO2020139845A1; EP3903448A4; EP3903448A1; CN113875187A; US11508484B1; JP7617002B2; JP2022515534A

Abstract

Systems and methods have been developed to increase user compliance and adherence to a variety of devices and services, including respiratory therapy devices, exercise equipment, and online or other software services. For example, in some examples, the disclosed techniques may monitor usage data output from a respiratory therapy device, exercise equipment, or computer software program to determine when a user may terminate or reduce usage within a specified time window based on usage trends. The tagging user may also trigger further actions to automatically intervene before the user terminates the participation in the service.

Description

Prediction of usage or compliance

Technical Field

1.1 Technical field

The present technology relates to one or more of screening, diagnosis, monitoring, treatment, prevention and amelioration of respiratory related disorders. The present technology also relates to medical devices or apparatus and uses thereof. The present technology also relates to compliance, use, or prediction of use withdrawal for respiratory therapy devices, websites and other software, exercise equipment, drivers of on-demand taxi services, or other applications.

1.2 Description of related Art

1.2.1 Human respiratory System and disorders thereof

The respiratory system of the body facilitates gas exchange. The nose and mouth form the entrance to the patient's airway.

The airways comprise a series of branches that become narrower, shorter and more numerous as the branch airways penetrate deeper into the lungs. The main function of the lungs is gas exchange, allowing oxygen to enter venous blood from the inhaled air and to expel carbon dioxide in the opposite direction. The trachea is divided into left and right main bronchi, which are ultimately subdivided into end bronchioles. The bronchi constitute the airway and do not participate in gas exchange. Further airway division results in respiratory bronchioles and ultimately alveoli. The alveolar region of the lung is where gas exchange occurs and is known as the respiratory tract. See, respiratory physiology (Respiratory Physiology), 9 th edition published by John b.west, lippincott Williams & Wilkins in 2012.

There are a range of respiratory disorders. Certain disorders may be characterized by specific events such as apneas, hypopneas, and hyperbreaths.

Examples of respiratory disorders include Obstructive Sleep Apnea (OSA), tidal breathing (CSR), respiratory insufficiency, obesity hyper-ventilation syndrome (OHS), chronic Obstructive Pulmonary Disease (COPD), neuromuscular disease (NMD), and chest wall disorders.

Obstructive Sleep Apnea (OSA) is a form of Sleep Disordered Breathing (SDB) characterized by events involving occlusion or blockage of the upper airway during sleep. This is caused by abnormally small upper airways in the tongue, soft palate and posterior oropharyngeal wall areas plus normal loss of muscular tone during sleep. The condition causes the affected patient to stop breathing, typically for a period of 30 seconds to 120 seconds, sometimes 200 to 300 times per night. It often causes excessive daytime sleepiness, and may lead to cardiovascular disease and brain damage. Although the affected person may not be aware of the problem, this syndrome is a disorder that is particularly common in overweight men in middle age. See U.S. Pat. No. 4,944,310 (Sullivan).

Tidal breathing (CSR) is another form of sleep disordered breathing. CSR is an obstacle to the respiratory controller of a patient, where there are alternating periods of rhythmic waxing and desired ventilation called CSR periods. CSR is characterized by repeated deoxygenation and reoxidation of arterial blood. CSR can be detrimental due to repeated hypoxia. In some patients, CSR is associated with repeated arousal from sleep, which results in severe sleep disruption, increased sympathetic activity, and increased afterload. See U.S. Pat. No. 6,532,959 (Berthon-Jones).

Respiratory failure is a covered term for respiratory disorders in which the lungs cannot inhale enough oxygen or exhale enough CO ₂ to meet the needs of the patient. Respiratory failure may encompass some or all of the following disorders.

Patients with respiratory insufficiency, a form of respiratory failure, may experience abnormal shortness of breath while exercising.

Obesity hyper-ventilation syndrome (OHS) is defined as a combination of severe obesity and awake chronic hypercapnia, with no other known causes of hypoventilation. Symptoms include dyspnea, morning headaches, and daytime sleepiness.

Chronic Obstructive Pulmonary Disease (COPD) includes any of a group of lower airway diseases that share some common features. These include increased resistance to air movement, prolonged expiratory phases of respiration, and loss of normal elasticity of the lungs. Examples of COPD are emphysema and chronic bronchitis. COPD is caused by chronic smoking (major risk factor), occupational exposure, air pollution and genetic factors. Symptoms include exertion dyspnea, chronic cough, and excessive phlegm.

Neuromuscular disease (NMD) is a broad term that encompasses many diseases and afflictions that impair muscle function directly via intrinsic muscle pathology or indirectly via neuropathology. Some NMD patients are characterized by progressive muscle damage, resulting in loss of walking ability, wheelchair binding, dysphagia, weakness of respiratory muscles, and ultimately death from respiratory failure. Neuromuscular disorders can be classified into (i) fast-progressing disorders characterized by muscle damage worsening within months and leading to death within years (e.g., juvenile Amyotrophic Lateral Sclerosis (ALS) and Duchenne Muscular Dystrophy (DMD), (ii) variable or slow-progressing disorders characterized by muscle damage worsening for years and only slightly reducing the life expectancy (e.g., limb girdle, shoulder humerus and myotonic muscular dystrophy), symptoms of NMD respiratory failure include increased general weakness, dysphagia, dyspnea at exertion and rest, fatigue, somnolence, morning headache, and difficulty in attention and mood changes.

Chest wall disorders are a group of chest deformities that result in inefficient coupling between the respiratory muscles and the chest. These disorders are often characterized by restrictive defects and have the potential for long-term hypercarbonated respiratory failure. Scoliosis and/or kyphosis can lead to severe respiratory failure. Symptoms of respiratory failure include dyspnea, peripheral edema, sitting breathing, recurrent chest infections, morning headaches, fatigue, poor sleep quality, and loss of appetite.

A range of therapies have been used to treat or ameliorate such conditions. In addition, other healthy individuals can utilize such treatments to prevent the occurrence of respiratory disorders. However, these have a number of disadvantages.

1.2.2 Therapy

Various therapies, such as Continuous Positive Airway Pressure (CPAP) therapy, non-invasive ventilation (NIV), and Invasive Ventilation (IV), have been used to treat one or more of the respiratory disorders described above.

1.2.2.1 Respiratory pressure therapy

Continuous Positive Airway Pressure (CPAP) has been used to treat Obstructive Sleep Apnea (OSA). The mechanism of action is continuous positive airway pressure as a pneumatic splint and may prevent upper airway obstruction, such as by pushing the soft palate and tongue forward and away from the posterior oropharyngeal wall. Treatment of OSA by CPAP therapy may be voluntary, so if the patient finds the device for providing such therapy uncomfortable, difficult to use, expensive, and unsightly, they may choose not to follow the therapy.

Non-invasive ventilation (NIV) provides ventilation support to a patient through the upper airway to assist the patient in breathing and/or to maintain adequate oxygen levels in the body by performing some or all of the respiratory effort. The ventilation support is provided via a non-invasive patient interface. NIV has been used in the treatment of CSR and respiratory failure in forms such as OHS, COPD, NMD and chest wall disorders. In some forms, the comfort and effectiveness of these therapies may be improved.

Invasive Ventilation (IV) provides ventilation support to a patient who is no longer able to breathe effectively on his own and who can be provided using a tracheostomy tube. In some forms, the comfort and effectiveness of these therapies may be improved.

1.2.2.2 Flow therapy

Not all respiratory therapies are intended to deliver a prescribed therapeutic pressure. Some respiratory therapies aim to deliver a prescribed respiratory volume, perhaps by targeting the flow distribution for a target duration. In other cases, the interface to the patient's airway is "open" (unsealed), and respiratory therapy may only supplement the patient's own spontaneous breathing. In one example, high Flow Therapy (HFT) is the provision of a continuous, heated, humidified flow of air to the airway inlet through an unsealed or open patient interface at a "therapeutic flow" that remains substantially constant throughout the respiratory cycle. The therapeutic flow is nominally set to exceed the peak inspiratory flow of the patient. HFT has been used to treat OSA, CSR, COPD and other respiratory disorders. One mechanism of action is that high flow air at the entrance to the airway increases ventilation efficiency by flushing or flushing exhaled CO ₂ from the patient's anatomical dead space. Thus, HFT is sometimes referred to as Dead Space Therapy (DST). In other flow therapies, the therapeutic flow rate may follow a curve that varies with the respiratory cycle.

Another form of flow therapy is long-term oxygen therapy (LTOT) or supplemental oxygen therapy. A physician may prescribe that a continuous flow of oxygen-enriched gas of a particular oxygen concentration (from 21%, oxygen fraction in ambient air to 100%) be delivered to the airway of a patient at a particular flow rate (e.g., 1 Liter Per Minute (LPM), 2LPM, 3LPM, etc.).

1.2.2.3 Oxygen supplementation

For some patients, oxygen therapy may be combined with respiratory pressure therapy or HFT by adding supplemental oxygen to the pressurized air stream. When oxygen is added in respiratory pressure therapy, this is referred to as RPT with supplemental oxygen. When oxygen is added to HFT, the resulting therapy is referred to as HFT with supplemental oxygen.

1.2.3 Treatment System

These respiratory therapies may be provided by a treatment system or apparatus. Such systems and devices may also be used to screen, diagnose, or monitor a condition without treating it.

Respiratory therapy systems may include respiratory pressure therapy devices (RPT devices), air circuits, humidifiers, patient interfaces, oxygen sources, and data management.

Another form of treatment system is a mandibular repositioning device.

1.2.3.1 Patient interface

The patient interface may be used to connect the breathing apparatus to its wearer, for example, by providing an air flow to an inlet of the airway. The air flow may be provided into the patient's nose and/or mouth via a mask, into the mouth via a tube, or into the patient's trachea via an autogenous cutting tube. Depending on the treatment to be applied, the patient interface may form a seal (e.g., a seal) with an area of the patient's face to facilitate delivery of the gas at a positive pressure of about 10cmH ₂ O relative to ambient pressure at a pressure that varies sufficiently from ambient pressure to effect the treatment. For other forms of therapy, such as oxygen delivery, the patient interface may not include a seal sufficient to facilitate delivery of a supply of gas at a positive pressure of about 10cmH ₂ O to the airway.

Some other mask systems may not be functionally suitable for use in the art. For example, a purely decorative mask may not be able to maintain proper pressure. Mask systems for underwater swimming or diving may be configured to prevent ingress of water at higher pressure from the outside, but not to maintain the internal air at a pressure above ambient pressure.

Some masks may be clinically detrimental to the present technique, for example, if they block airflow through the nose and only allow airflow through the mouth.

If some masks require a patient to insert a portion of the mask structure into their mouth to create and maintain a seal via their lips, it may be uncomfortable or impractical for the present technique.

Some masks may not be practical for use while sleeping, such as when the head is lying on the side on a pillow and sleeping in a bed.

The design of patient interfaces presents a number of challenges. The face has a complex three-dimensional shape. The size and shape of the nose varies significantly from person to person. Since the head contains bone, cartilage and soft tissue, different regions of the face respond differently to mechanical forces. The jawbone or mandible may be moved relative to the other bones of the skull. The entire head may be moved during respiratory therapy.

Because of these challenges, some masks face one or more of the problems of being obtrusive, unsightly, expensive, inconsistent, difficult to use, and uncomfortable, particularly when worn for extended periods of time or when the patient is unfamiliar with the system. Wrong sized masks may lead to reduced compliance, reduced comfort, and poor patient outcome. Masks designed for pilots only, masks designed to be part of personal protective equipment (e.g., filtering masks), SCUBA masks, or masks designed for applying anesthetic agents are acceptable for their original application, but such masks are not ideal as comfortable for wearing for long periods of time (e.g., several hours). Such discomfort may lead to reduced patient compliance with the treatment. This is especially true if the mask is worn during sleep.

CPAP therapy is very effective in treating certain respiratory disorders, provided that the patient is treatment-compliant. If the mask is uncomfortable or difficult to use, the patient may not be compliant with the treatment. Since patients are often advised to regularly clean their masks, if the masks are difficult to clean (e.g., difficult to assemble or disassemble), the patients may not clean their masks, which may affect patient compliance.

While masks for other applications (e.g., navigator) may not be suitable for treating sleep disordered breathing, masks designed for treating sleep disordered breathing may be suitable for other applications.

For these reasons, patient interfaces for delivering CPAP during sleep form a different field.

1.2.3.1.1 Seal forming structure

The patient interface may include a seal-forming structure. Because the seal-forming structure is in direct contact with the patient's face, the shape and configuration of the seal-forming structure can directly affect the effectiveness and comfort of the patient interface.

The patient interface may be characterized in part by the design intent of the seal-forming structure to engage the face in use. In one form of patient interface, the seal-forming structure may include a first sub-portion that forms a seal around the left naris and a second sub-portion that forms a seal around the right naris. In one form of patient interface, the seal-forming structure may comprise a single element that, in use, surrounds both nostrils. Such a single element may be designed to cover, for example, the upper lip region and the nasal bridge region of the face. In one form of patient interface, the seal-forming structure may comprise an element that in use surrounds the mouth region, for example by forming a seal on the lower lip region of the face. In one form of patient interface, the seal-forming structure may comprise a single element that in use surrounds both nostrils and mouth regions. These different types of patient interfaces may be variously named by their manufacturers, including nasal masks, full face masks, nasal pillows, nasal sprays, and oral nasal masks.

A seal-forming structure that may be effective in one region of a patient's face may not fit in another region, for example, because of the differences in shape, structure, variability, and sensitive areas of the patient's face. For example, seals on swimming goggles covering the forehead of a patient may not be suitable for use over the nose of a patient.

Certain seal-forming structures may be designed for mass production so that one design is suitable and comfortable and effective for a wide range of different face shapes and sizes. To the extent there is a mismatch between the shape of the patient's face and the seal-forming structure of a mass-produced patient interface, one or both must be accommodated to form a seal.

One type of seal-forming structure extends around the periphery of the patient interface and is intended to seal against the patient's face when a force is applied to the patient interface while the seal-forming portion is in face-to-face engagement with the patient's face. The seal-forming structure may comprise an air or fluid filled pad, or a molded or shaped surface of a resilient sealing element made of an elastomer such as rubber. For this type of seal-forming structure, if there is insufficient fit, there will be a gap between the seal-forming structure and the face, and additional force will be required to force the patient interface against the face to effect a seal.

Another type of seal-forming structure incorporates a sheet-like seal of thin material positioned around the perimeter of the mask to provide a self-sealing action against the patient's face when positive pressure is applied within the mask. Similar to the previous forms of seal forming portions, if the fit between the face and mask is not good, additional force may be required to achieve the seal, or the mask may leak. Furthermore, if the shape of the seal-forming structure does not match the shape of the patient, it may buckle or bend during use, resulting in leakage.

Another type of seal-forming structure may include friction-fit elements, for example, for insertion into nostrils, however some patients find these uncomfortable.

Another form of seal-forming structure may use an adhesive to effect the seal. Some patients may find it inconvenient to apply and remove adhesive from their face often.

A series of patient interface seal forming structural techniques are disclosed in WO 1998/004,310, WO 2006/074,513, WO 2010/135,785, assigned to RESMED LIMITED.

One form of nasal pillow is found in Adam Circuit (Adam Circuit) manufactured by Puritan Bennett. Another nasal pillow or snuff is the subject of U.S. Pat. No. 4,782,832 (Trimble et al) assigned to Puritan-Bennett corporation.

1.2.3.1.2 Positioning and stabilization

The seal-forming structure of a patient interface for positive air pressure therapy is subjected to a corresponding force of air pressure to break the seal. Accordingly, various techniques have been used to position the seal-forming structure and maintain it in sealing relation with the appropriate portion of the face.

One technique is to use an adhesive. See, for example, U.S. patent application publication No. US 2010/0000534. However, some people may feel uncomfortable with the use of adhesives.

Another technique is to use one or more straps and/or stabilizing the harness. Many such harnesses are subject to one or more of discomfort, bulk, discomfort, and use.

1.2.3.2 Respiratory Pressure Treatment (RPT) devices

Respiratory Pressure Therapy (RPT) devices may be used alone or as part of a system to deliver one or more of the various therapies described above, such as by operating the device to generate an air stream for delivery to an airway interface. The airflow may be pressure controlled (for respiratory pressure therapy) or flow controlled (for flow therapy, such as HFT). Thus, the RPT device may also be used as a flow therapy device. Examples of RPT devices include CPAP devices and ventilators. Examples of RPT devices include CPAP devices and ventilators.

Barometric pressure generators are known in the field of applications such as industrial scale ventilation systems. However, air pressure generators for medical applications have specific requirements that are not met by more common air pressure generators, such as reliability, size, and weight requirements of medical devices. Furthermore, even devices designed for medical use may have drawbacks with respect to one or more of comfort, noise, ease of use, efficacy, size, weight, manufacturability, cost, and reliability.

One example of a particular requirement for some RPT devices is noise.

Noise output level table of existing RPT devices (only one sample, measured in CPAP mode of 10cmH ₂ O using the test method specified in IS 03744).

RPT device name	A-weighted sound pressure level dB (A)	Years (approximately)
			C series Tango ^TM	31.9	2007
C series Tango with humidifier ^TM	33.1	2007
			S8 Escape^TMII	30.5	2005
S8 Escape ^TM II with H4i ^TM humidifier	31.1	2005
			S9 AutoSet^TM	26.5	2010
S9 AutoSet with H5i humidifier ^TM	28.6	2010

One known RPT device for treating sleep disordered breathing is the S9 sleep treatment system manufactured by RESMED LIMITED. Another example of an RPT device is a ventilator. A RESMED STELLAR ^TM series of ventilators, such as adult and pediatric ventilators, may provide support for invasive and non-invasive, independent ventilation for a range of patients to treat a variety of conditions, such as, but not limited to, NMD, OHS, and COPD.

RESMED ELIS e ^TM ventilator and RESMED VS III ^TM ventilator can provide support for invasive and non-invasive dependent ventilation suitable for adult or pediatric patients for the treatment of a variety of disorders. These ventilators provide a volumetric ventilation mode and a pneumatic ventilation mode with either a single-limb circuit or a dual-limb circuit. RPT devices typically contain a pressure generator, such as a motor-driven blower or compressed gas reservoir, and are configured to supply a flow of air to the airway of a patient. In some cases, the flow of air may be provided to the airway of the patient at a positive pressure. The outlet of the RPT device is connected via an air circuit to a patient interface such as those described above.

The designer of the device may present an unlimited number of choices that may be made. Design criteria often conflict, which means that some design choices are far from routine or unavoidable. Furthermore, certain aspects of comfort and efficacy may be highly sensitive to small and subtle changes in one or more parameters.

1.2.3.3 Humidifier

Delivering an air flow without humidification can lead to airway dryness. The use of a humidifier with an RPT device and patient interface generates humidified gases, minimizing nasal mucosa desiccation and increasing patient airway comfort. Furthermore, in colder climates, warm air, which is typically applied to the facial area in and around the patient interface, is more comfortable than cold air.

A range of artificial humidification devices and systems are known, however they may not meet the specific requirements of medical humidifiers.

Medical humidifiers are used to increase the humidity and/or temperature of an air stream relative to ambient air when needed, typically at a location where a patient is asleep or resting (e.g., in a hospital). Medical humidifiers for bedside placement may be small. The medical humidifier may be configured to only humidify and/or heat the air flow delivered to the patient, and not to humidify and/or heat the patient's surroundings. Room-based systems (e.g. saunas, air conditioners or evaporative coolers) may also humidify the air breathed by the patient, for example, however these systems also humidify and/or heat the whole room, which may cause discomfort to the occupants. Furthermore, medical humidifiers may have more stringent safety constraints than industrial humidifiers

While many medical humidifiers are known, they may have one or more drawbacks. Some medical humidifiers may provide inadequate humidification, and some patients may have difficulty or inconvenience in use.

1.2.3.4 Data management

There may be clinical reasons for obtaining data to determine whether a patient prescribed respiratory therapy has "complied with," e.g., the patient has used their RPT device according to one or more "compliance rules. One example of a compliance rule for CPAP therapy is to require the patient to use the respiratory pressure treatment device on at least twenty-one (21) of thirty (30) consecutive days, at least four hours per night, in order to be considered compliance. To determine patient compliance, a provider of the RPT device (such as a healthcare provider) may manually obtain data describing the treatment of the patient using the RPT device, calculate usage over a predetermined period of time, and compare to compliance rules. Once the healthcare provider has determined that the patient has used their RPT device according to the compliance rules, the healthcare provider can notify third parties that the patient is compliant.

Other aspects of patient treatment may exist that would benefit from communication of treatment data to a third party or external system.

Existing processes of communicating and managing such data may be one or more of expensive, time consuming, and error prone.

1.2.3.5 Vent technique

Some forms of treatment systems may include an exhaust port to allow for the removal of exhaled carbon dioxide. The vent may allow gas to flow from an interior space of a patient interface (e.g., a plenum) to an exterior of the patient interface (e.g., to the ambient environment).

The vent may comprise a vent and the gas may flow through the vent in use of the mask. Many such vents are noisy. Others may clog during use, providing insufficient flushing. Some vents may interfere with sleep of the bed partner 1100 of the patient 1000, such as by noise or concentrated airflow.

A number of improved mask ventilation techniques have been developed by rismate limited. See International patent application publication No. WO1998/034,665, international patent application publication No. WO2000/078,381, U.S. Pat. No. 6,581,594, U.S. patent application publication No. US2009/0050156, U.S. patent application publication No. US 2009/0044808.

Noise meter of existing mask (ISO 17510-2:2007, 10cmH ₂ O pressure at 1 m)

Only one sample was measured in CPAP mode at 10cmH ₂ O using the test method specified in ISO 3744

The sound pressure values of the various objects are listed below

1.2.4 Screening, diagnostic and monitoring System

Polysomnography (PSG) is a conventional system for diagnosing and monitoring heart-lung disorders and typically involves a clinical specialist to apply the system. PSG typically involves placing 15 to 20 contact sensors on the patient to record various body signals, such as electroencephalograms (EEG), electrocardiography (ECG), electrooculography (EOG), electromyography (EMG), etc. PSG for sleep disordered breathing involves observing the patient in the clinic for two nights, for a pure diagnosis for one night, and for the clinician to titrate the treatment parameters for one night. Thus, PSG is expensive and inconvenient. In particular, it is not suitable for home screening/diagnosis/monitoring of sleep disordered breathing.

Screening and diagnosis generally describes identifying a condition from its signs and symptoms. Screening typically gives true/false results indicating whether the patient's SDB is severe enough to warrant further investigation, whereas diagnosis may yield clinically actionable information. Screening and diagnosis tend to be a one-time process, while monitoring of disease progression may continue indefinitely. Some screening/diagnostic systems are only suitable for screening/diagnosis, while some may also be used for monitoring.

Clinical professionals are able to adequately screen, diagnose, or monitor patients based on visual observations of PSG signals. However, there are situations where a clinical expert may not be available or where a clinical expert may not be affordable. Different clinical professionals may have different opinion on the pathology of a patient. Furthermore, a given clinical expert may apply different criteria at different times.

Disclosure of Invention

The present technology relates to providing medical devices for screening, diagnosing, monitoring, ameliorating, treating, or preventing respiratory disorders with one or more of improved comfort, cost, efficacy, ease of use, and manufacturability.

A first aspect of the present technology relates to an apparatus for screening, diagnosing, monitoring, ameliorating, treating or preventing a respiratory disorder.

Another aspect of the present technology relates to methods used in web site or computer software usage and monitoring, as well as usage monitoring including other applications for on-demand taxi services and drivers of exercise equipment.

One aspect of certain forms of the present technology provides methods and/or apparatus to improve patient compliance with respiratory therapy, websites, and other software programs that require user participation, exercise equipment, drivers for on-demand taxi services, or other applications.

One form of the present technology includes a respiratory therapy device, a computer system, exercise equipment, or other system that outputs usage data, and a controller that processes the usage data to determine whether a user or patient will reduce usage of the respiratory therapy device, exercise equipment, computer system, and software or other applications within a time window.

Another aspect of one form of the present technology is to determine the likelihood that a user using a respiratory therapy device computer system, exercise equipment, or other system will exit within a specified time window.

Another aspect of one form of the present technology is to identify non-usage (non-usage) days, average number of uses per day, and weekly trend of standard deviation of usage to a user using a respiratory therapy device computer system, exercise equipment, or other system to determine whether the user is logged out or is reduced in usage.

Another aspect of one form of the present technique is to use random forest and logistic regression algorithms to process usage data output from respiratory therapy device computer systems, exercise equipment, or other systems to determine whether a user will exit within a specified time window (e.g., two weeks, one week, or three weeks).

The described methods, systems, apparatus and devices may be implemented to improve the functionality of a processor, such as a processor of a special purpose computer, a respiratory monitor and/or a respiratory therapy device. Furthermore, the described methods, systems, devices, and apparatus may provide improvements in the art including automatic management, monitoring, and/or treatment of respiratory conditions, such as sleep disordered breathing.

Of course, some of these aspects may form sub-aspects of the present technology. Various aspects of the sub-aspects and/or aspects may be combined in various ways and also constitute other aspects or sub-aspects of the present technology.

Other features of the present technology will become apparent from the following detailed description, abstract, drawings, and claims.

Drawings

The present technology is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which like reference numerals refer to similar elements and in which:

3.1 treatment System

Fig. 1A shows a system that includes a patient 1000 wearing a patient interface 3000 in the manner of a nasal pillow receiving a supply of air under positive pressure from an RPT device 4000. Air from the RPT device 4000 is humidified in a humidifier 5000 and flows along an air circuit 4170 to the patient 1000. A bed partner 1100 is also shown. The patient sleeps in a supine sleeping position.

Fig. 1B shows a system that includes a patient 1000 wearing a patient interface 3000 in the manner of a nasal mask receiving a supply of air under positive pressure from an RPT device 4000. Air from the RPT device is humidified in the humidifier 5000 and flows along the air circuit 4170 to the patient 1000.

Fig. 1C shows a system that includes a patient 1000 wearing a patient interface 3000 in a full-face mask receiving a supply of air under positive pressure from an RPT device 4000. Air from the RPT device is humidified in the humidifier 5000 and flows along the air circuit 4170 to the patient 1000. The patient sleeps in a side lying sleeping position.

3.2 Respiratory System and facial anatomy

Fig. 2A shows an overview of the human respiratory system, which includes nasal and oral cavity, throat, vocal cords, esophagus, trachea, bronchi, lung, alveolar sacs, heart and diaphragm.

3.3 Patient interface

Fig. 3A illustrates a patient interface in the form of a nasal mask in accordance with one form of the present technique.

3.4 RPT device

Fig. 4A illustrates an exploded view of an RPT device in one form in accordance with the present technique.

Fig. 4B is a schematic diagram of the pneumatic path of an RPT device in one form in accordance with the present technique. The upstream and downstream directions are indicated with reference to the blower and patient interface. The blower is defined upstream of the patient interface and the patient interface is defined downstream of the blower, regardless of the actual flow direction at any particular moment. An article located in the pneumatic path between the blower and the patient interface is downstream of the blower and upstream of the patient interface.

Fig. 4C is a schematic diagram of electrical components of an RPT device in one form in accordance with the present technique.

Fig. 4D is a schematic diagram of an algorithm implemented in an RPT device in accordance with one form of the present technique.

Fig. 4E is a flow chart illustrating a method performed by the treatment engine module of fig. 4D in accordance with one form of the present technique.

3.5 Humidifier

Figure 5A is an isometric view of a humidifier in one form in accordance with the present technique.

Figure 5B illustrates an isometric view of a humidifier in one form in accordance with the present technique, showing the humidifier reservoir 5110 removed from the humidifier reservoir base 5130.

Figure 5C shows a schematic diagram of a humidifier in one form in accordance with the present technique.

3.6 Respiratory waveform

Fig. 6A shows a model representative respiratory waveform of a person while sleeping.

Fig. 6B shows selected polysomnography channels (pulse oximetry, flow, chest movement, and abdominal movement) of a patient during non-REM sleep breathing, typically in excess of about 90 seconds.

3.7 Screening, diagnostic and monitoring System

Fig. 7A shows a patient undergoing Polysomnography (PSG). The patient sleeps in a supine sleeping position.

Fig. 7B shows a monitoring device for monitoring a patient condition. The patient sleeps in a supine sleeping position.

3.8 Data Transmission

Fig. 8A is a block diagram of a system that receives usage data and other data from a respiratory therapy device.

Fig. 8B is a block diagram of an alternative system that receives usage data and other data from a respiratory therapy device.

3.9 Predictive reduction or termination of treatment

Fig. 9 shows a flow chart illustrating a method of predicting patient compliance based on usage data output from a respiratory therapy device.

Fig. 10 shows a schematic diagram of an example of a time window of usage data considered in an algorithm applied with the disclosed technology.

Detailed Description

Before the present technology is described in further detail, it is to be understood that this technology is not limited to particular examples described herein, as such may vary. It is also to be understood that the terminology used in the present disclosure is for the purpose of describing particular examples described herein only and is not intended to be limiting.

The following description is provided with respect to various examples that may share one or more common characteristics and/or features. It should be understood that one or more features of any example may be combined with one or more features of other examples. In addition, in any of the examples, any single feature or combination of features may constitute further examples.

4.1 Treatment

In one form, the present technique includes a method for treating a respiratory disorder, the method including the step of applying positive pressure to an airway inlet of a patient 1000.

In some examples of the present technology, the air supply under positive pressure is provided to the nasal passages of the patient via one or both nostrils.

In some examples of the present technology, oral breathing is restricted, constrained, or prevented.

4.2 Treatment System

In one form, the present technology comprises an apparatus or device for treating a respiratory disorder. The apparatus or device may include an RPT device 4000 for supplying pressurized air to the patient 1000 via an air circuit 4170 to the patient interface 3000 or 3800.

4.3 Patient interface

A non-invasive patient interface 3000 in accordance with one aspect of the present technique includes functional aspects of a seal-forming structure 3100, a plenum chamber 3200, a positioning and stabilizing structure 3300, a vent 3400, a form of connection port 3600 for connection to an air circuit 4170, and a forehead support 3700. In some forms, the functional aspects may be provided by one or more physical components. In some forms, one physical component may provide one or more functional aspects. In use, the seal-forming structure 3100 is arranged to surround an entrance to the patient's airway in order to supply positive pressure air to the airway.

The unsealed patient interface 3800 in the form of a nasal catheter includes nasal prongs 3810a, 3810b that can deliver air to respective nostrils of the patient 1000. Such prongs typically do not form a seal with the inner or outer skin surface of the nostrils. Air may be delivered to the nasal prongs through one or more air supply lumens 3820a, 3820b coupled to the nasal catheter 3800. Lumens 3820a, 3820b lead from nasal catheter 3800 to RT devices that generate air flow at high flow rates. A "vent" at the unsealed patient interface 3800 is a passage between the ends of the prongs 3810a and 3810b of the cannula 3800 to the atmosphere via the patient's nostrils through which excess airflow escapes to the ambient environment.

If the patient interface is unable to comfortably deliver a minimum level of positive pressure to the airway, the patient interface may not be suitable for respiratory pressure therapy.

4.3.1 Seal formation Structure

In one form of the present technique, the seal forming structure 3100 provides a target seal forming region and may additionally provide a cushioning function. The target seal forming area is an area on the seal forming structure 3100 where sealing may occur. The area where the seal actually occurs-the actual sealing surface-may vary from day to day and from patient to patient within a given treatment period, depending on a number of factors including, for example, the location of the patient interface on the face, the tension in the positioning and stabilizing structure, and the shape of the patient's face.

In one form, the target seal-forming area is located on an outer surface of the seal-forming structure 3100.

In some forms of the present technology, the seal-forming structure 3100 is constructed of a biocompatible material, such as silicone rubber.

The seal forming structure 3100 according to the present technology may be constructed of a soft, flexible, resilient material such as silicone.

In certain forms of the present technology, a system is provided that includes more than one seal-forming structure 3100, each configured to correspond to a different size and/or shape range. For example, the system may include one form of seal forming structure 3100 that is suitable for large sized heads but not small sized heads, and another suitable for small sized heads but not large sized heads.

4.3.1.1 Sealing mechanism

In one form, the seal-forming structure includes a sealing flange that utilizes a pressure-assisted sealing mechanism. In use, the sealing flange can readily respond to positive system pressure in the interior of the plenum chamber 3200 acting on its underside, bringing it into tight sealing engagement with the face. The pressure assist mechanism may act in conjunction with elastic tension in the positioning and stabilizing structure.

In one form, the seal forming structure 3100 includes a sealing flange and a support flange. The sealing flange comprises a relatively thin member having a thickness of less than about 1mm, such as about 0.25mm to about 0.45mm, which extends around the perimeter of the plenum chamber 3200. The support flange may be relatively thicker than the sealing flange. The support flange is disposed between the sealing flange and an edge of the plenum chamber 3200 and extends at least a portion of the way around the perimeter. The support flange is or comprises a spring-like element and is adapted to support the sealing flange against buckling in use.

In one form, the seal-forming structure may include a compression seal portion or a gasket seal portion. In use, the compression seal portion or the gasket seal portion is constructed and arranged to be in a compressed state, for example as a result of elastic tension in the positioning and stabilising structure.

In one form, the seal-forming structure includes a tensioning portion. In use, the tensioning portion is held in tension, for example by adjacent regions of the sealing flange.

In one form, the seal-forming structure comprises a region having a tacky or adhesive surface.

In some forms of the present technology, the seal-forming structure may include one or more of a pressure-assisted seal flange, a compression seal portion, a gasket seal portion, a tension portion, and a portion having an adhesive or bonding surface.

4.3.1.2 Nasal bridge or nasal ridge region

In one form, the non-invasive patient interface 3000 includes a seal-forming structure that forms a seal over a nasal bridge or ridge region of a patient's face in use.

In one form, the seal-forming structure includes a saddle region configured to form a seal over a nasal bridge region or nasal ridge region of a patient's face.

4.3.1.3 Upper lip region

In one form, the non-invasive patient interface 3000 includes a seal-forming structure that forms a seal when used on an upper lip region (i.e., an upper lip portion) of a patient's face.

In one form, the seal-forming structure comprises a saddle region configured to form a seal on an upper lip region of a patient's face in use.

4.3.1.4 Chin region

In one form, the non-invasive patient interface 3000 includes a seal-forming structure that forms a seal when used on the chin area of the patient's face.

In one form, the seal-forming structure includes a saddle region configured to form a seal when used on a chin region of a patient's face.

4.3.1.5 Forehead area

In one form, the seal-forming structure forms a seal over a forehead region of a patient's face in use. In this form, the plenum chamber may cover the eye in use.

4.3.1.6 Nasal pillows

In one form, the seal-forming structure of the non-invasive patient interface 3000 includes a pair of nasal masks or pillows, each constructed and arranged to form a seal with a respective nostril of the patient's nose.

A nasal pillow in accordance with one aspect of the present technique includes a frustoconical body at least a portion of which forms a seal on a floor of a patient's nose, a handle, and a flexible region on the floor of the frustoconical body and connecting the frustoconical body to the handle. In addition, the nasal pillow attachment structure of the present technology includes a flexible region adjacent the base of the handle. The flexible regions may cooperate to facilitate a universal joint structure that is adaptable with relative movement of both displacement and angle between the frustoconical and nasal pillow connected structures. For example, the frustoconical position may be axially moved toward the stem-connecting structure.

4.3.2 Plenum

The plenum chamber 3200 has a perimeter shaped to complement the surface contour of an average person's face in the area where a seal will be formed in use. In use, the edges of the plenum chamber 3200 are positioned immediately adjacent to the adjacent surface of the face. The seal forming structure 3100 provides the actual contact with the face. The seal forming structure 3100 may extend around the entire perimeter of the plenum chamber 3200 in use. In some forms, the plenum chamber 3200 and seal forming structure 3100 are formed from a single sheet of homogeneous material.

In some forms of the present technology, the plenum chamber 3200 does not cover the patient's eyes in use. In other words, the perforations are outside the pressurized volume defined by the plenum. Such forms tend to be less invasive and/or more comfortable for the wearer, which may improve compliance with the treatment.

In some forms of the present technology, the plenum chamber 3200 is constructed of a transparent material, such as transparent polycarbonate. The use of a transparent material may reduce the prominence of the patient interface and help to improve compliance with the therapy. The use of transparent materials may help a clinician to see how the patient interface is positioned and functioning.

In some forms of the present technology, the plenum chamber 3200 is constructed of a translucent material. The use of a transparent material may reduce the prominence of the patient interface and help to improve compliance with the therapy.

4.3.3 Positioning and stabilization Structure

The seal-forming structure 3100 of the patient interface 3000 of the present technology may be maintained in a sealed state by a positioning and stabilizing structure 3300 when in use.

In one form, the positioning and stabilizing structure 3300 provides a retention force that is at least sufficient to overcome the effect of positive pressure in the plenum chamber 3200 to lift off the face.

In one form, the positioning and stabilizing structure 3300 provides a retention force to overcome the force of gravity on the patient interface 3000.

In one form, the positioning and stabilizing structure 3300 provides retention as a safety margin to overcome potential effects of damaging forces on the patient interface 3000, such as accidental interference from tube drag or with the patient interface.

In one form of the present technique, a positioning and stabilizing structure 3300 is provided that is configured in a manner consistent with being worn by a patient while sleeping. In one example, the positioning and stabilizing structure 3300 has a low profile or cross-sectional thickness to reduce the perceived or actual volume of the device. In one example, the positioning and stabilizing structure 3300 includes at least one strap that is rectangular in cross-section. In one example, the positioning and stabilizing structure 3300 includes at least one flat strap.

In one form of the present technique, a positioning and stabilizing structure 3300 is provided that is configured to be less bulky and cumbersome to prevent a patient from lying in a supine sleeping position, with the back area of the patient's head on a pillow.

In one form of the present technique, a positioning and stabilizing structure 3300 is provided that is configured to be less bulky and cumbersome to prevent a patient from lying in a side sleep position, with a side region of the patient's head on a pillow.

In one form of the present technique, the positioning and stabilizing structure 3300 is provided with a decoupling portion located between a front portion of the positioning and stabilizing structure 3300 and a rear portion of the positioning and stabilizing structure 3300. The decoupling portion is not resistant to compression and may be, for example, a flexible band or a soft band. The decoupling portion is constructed and arranged such that the presence of the decoupling portion prevents forces acting on the rear portion from being transmitted along the positioning and stabilizing structure 3300 and breaking the seal when the patient lays their head on the pillow.

In one form of the present technique, the positioning and stabilizing structure 3300 comprises a belt configured from a laminate of a fabric patient contacting layer, a foam inner layer, and a fabric outer layer. In one form, the foam is porous to allow moisture (e.g., sweat) to pass through the belt. In one form, the outer layer of fabric comprises loop material for partial engagement with the hook material.

In certain forms of the present technology, the positioning and stabilizing structure 3300 comprises a strap that is extendable, e.g., elastically extendable. For example, the strap may be configured to be in tension when in use and to direct a force to bring the seal-forming structure into sealing contact with a portion of the patient's face. In one example, the strap may be configured as a lace.

In one form of the present technique, the positioning and stabilizing structure comprises a first strap configured and arranged such that, in use, at least a portion of a lower edge of the first strap passes over an on-the-ear base of the patient's head and covers a portion of the parietal bone and not the occipital bone.

In one form of the present technology applicable to nasal only masks or to full face masks, the positioning and stabilizing structure comprises a second strap configured and arranged such that, in use, at least a portion of the upper edge of the second strap passes under the sub-aural base of the patient's head and covers or is located under the occiput of the patient's head.

In one form of the present technology applicable to nasal only masks or to full face masks, the positioning and stabilizing structure includes a third strap configured and arranged to interconnect the first strap and the second strap to reduce the tendency of the first strap and the second strap to separate from each other.

In certain forms of the present technology, the positioning and stabilizing structure 3300 comprises a strap that is bendable and, for example, non-rigid. This aspect has the advantage that the belt makes it more comfortable for the patient to lie on while sleeping.

In certain forms of the present technology, the positioning and stabilizing structure 3300 comprises a strap configured to be breathable to allow moisture to be transported through the strap.

In certain forms of the present technology, a system is provided that includes more than one positioning and stabilizing structure 3300, each configured to provide a retention force to correspond to a different range of sizes and/or shapes. For example, the system may include one form of positioning and stabilizing structure 3300 that is suitable for large-sized heads, but not for small-sized heads, while another form of positioning and stabilizing structure is suitable for small-sized heads, but not for large-sized heads.

In certain forms of the present technology, the stabilizing structure 3300 includes sensors configured to output data related to tensile or other related forces, stresses, or mechanical values along the longitudinal axes of the straps. In other examples, the stabilizing structure 3300 may include a pressure sensor on one side of the pressure of the sensing strip against the patient's head.

4.3.4 Vent

In one form, the patient interface 3000 includes a vent 3400 constructed and arranged to allow for flushing of exhaled gases, such as carbon dioxide.

In some forms, the vent 3400 is configured to allow continuous venting flow from the interior of the plenum chamber 3200 to the ambient environment while the pressure within the plenum chamber is positive relative to the ambient environment. The vent 3400 is configured such that the vent flow has a magnitude sufficient to reduce re-breathing of exhaled CO ₂ by the patient while maintaining therapeutic pressure in the plenum in use.

One form of vent 3400 in accordance with the present technology includes a plurality of holes, for example, about 20 to about 80 holes, or about 40 to about 60 holes, or about 45 to about 55 holes.

The vent 3400 may be located in the plenum chamber 3200. Alternatively, the vent 3400 is located in a decoupling structure (e.g., a swivel).

In one form of the present technology, the vent 3400 may include an acoustic sensor to determine whether vent noise emanates from the vent 3400. For example, an acoustic sensor on the vent 3400 may be compared to noise output from a remote sensor or a sensor on another component of the RPT device 4000 to determine noise associated with the vent 3400 as opposed to other components of the RPT device 4000.

4.3.5 Forehead support

In one form, patient interface 3000 includes forehead support 3700.

4.3.6 Port

In one form of the present technique, the patient interface 3000 includes one or more ports that allow access to the volume within the plenum chamber 3200. In one form, this allows the clinician to supply supplemental oxygen. In one form, this allows for direct measurement of a property of the gas within the plenum chamber 3200, such as pressure.

4.4 RPT device

An RPT device 4000 in accordance with one aspect of the present technology comprises mechanical, pneumatic, and/or electrical components and is configured to execute one or more algorithms 4300, such as any of all or part of the methods described herein. RPT device 4000 may be configured to generate an air flow for delivery to an airway of a patient, such as for treating one or more respiratory conditions described elsewhere in this document.

In one form, RPT device 4000 is configured and arranged to be capable of delivering an air flow in the range of-20L/min to +150L/min while maintaining a positive pressure of at least 6cmH ₂ O, or at least 10cmH ₂ O, or at least 20cmH ₂ O.

4.4.1 Mechanical and pneumatic components of RPT devices

The RPT device may include one or more of the following components in an overall unit. In one alternative, one or more of the following components may be provided as separate units.

4.4.1.1 Converter

The converter may be internal to the RPT device or external to the RPT device. The external transducer may be located on or form part of an air circuit (e.g., patient interface), for example. The external transducer may be in the form of a non-contact sensor, such as a doppler radar motion sensor that transmits or transfers data to the RPT device.

In one form of the present technique, one or more transducers 4270 are located upstream and/or downstream of pressure generator 4140. The one or more transducers 4270 may be configured and arranged to generate a signal representative of a characteristic of the air flow (such as flow, pressure, or temperature) at the point in the pneumatic path.

In one form of the present technology, one or more transducers 4270 may be located near the patient interface 3000 or 3800, containing one or more acoustic sensors.

In one form, the signal from the transducer 4270 may be filtered, such as by low pass, high pass, or band pass filtering.

4.4.1.1.1 Flow sensor

The flow sensor 4274 according to the present technology may be based on a differential pressure transducer, such as the SDP600 series differential pressure transducer from SENSIRION.

In one form, the central controller 4230 receives a signal representative of the flow from the flow sensor 4274.

4.4.1.1.2 Pressure sensor

The pressure sensor 4272 in accordance with the present technique is in fluid communication with the pneumatic path. An example of a suitable pressure transducer is the HONEYWELLASDX series of sensors. Another suitable pressure transducer is the sensor of the NPA series of GENERALELECTRIC.

In one form, the signal from the pressure sensor 4272 is received by the central controller 4230.

4.4.1.1.3 Motor speed converter

In one form of the present technique, the motor speed converter 4276 is used to determine the rotational speed of the motor 4144 and/or blower 4142. The motor speed signal from the motor speed converter 4276 may be provided to the treatment device controller 4240. The motor speed converter 4276 may be, for example, a speed sensor, such as a hall effect sensor.

4.4.2 CPG device electrical component

4.4.2.1 Power supply

The power supply 4210 may be located inside or outside the housing 4010 of the RPT device 4000.

In one form of the present technique, the power supply 4210 provides power only to the RPT device 4000. In another form of the present technique, the power supply 4210 provides power to both the RPT device 4000 and the humidifier 5000.

4.4.2.2 Input means

In one form of the present technology, RPT device 4000 includes one or more input devices 4220 in the form of buttons, switches, or dials to allow a person to interact with the device. The buttons, switches, or dials may be physical devices, or software devices accessible via a touch screen. In one form, the buttons, switches, or dials may be physically connected to the outer housing 4010, or in another form, may be in wireless communication with a receiver electrically connected to the central controller 4230.

In one form, the input device 4220 may be constructed and arranged to allow a person to select values and/or menu options.

4.4.2.3 Central controller

In one form of the present technique, the central controller 4230 is one or more processors adapted to control the RPT device 4000.

Suitable processors may include x86 INTEL processors based on ARM holdersA processor such as an STM32 series microcontroller from STMICROELECTRONICS. In some alternatives of the present technology, a 32-bit RISC CPU (such as an STR9 series microcontroller from ST MICROELECTRONICS) or a 16-bit RISC CPU (such as a processor from an MSP430 series microcontroller) manufactured by TEXAS INSTRMENTS may also be suitable.

In one form of the present technique, the central controller 4230 is a dedicated electronic circuit.

In one form, the central controller 4230 is an application specific integrated circuit. In another form, the central controller 4230 comprises discrete electronic components.

The central controller 4230 may be configured to receive one or more input signals from one or more converters 4270, one or more input devices 4220, and the humidifier 5000.

The central controller 4230 may be configured to provide output signals to one or more of the output device 4290, the treatment device controller 4240, the data communication interface 4280, and the humidifier 5000.

In some forms of the present technology, the central controller 4230 is configured to implement one or more methods described herein, such as one or more algorithms 4300 represented as a computer program stored in a non-transitory computer-readable storage medium (such as memory 4260). In some forms of the present technology, the central controller 4230 may be integrated with the RPT device 4000. However, in some forms of the present technology, some methods may be performed by a remotely located device. For example, the remotely located device may determine control settings for the ventilator or detect respiratory-related events by analyzing stored data (such as data from any of the sensors described herein).

4.4.2.4 Clock

The RPT device 4000 may comprise a clock 4232 connected to a central controller 4230.

4.4.2.5 Therapeutic device controller

In one form of the present technique, the treatment device controller 4240 is a treatment control module 4330 that forms part of an algorithm 4300 executed by the central controller 4230.

In one form of the present technique, the treatment device controller 4240 is a dedicated motor control integrated circuit. For example, in one form, a MC33035 brushless DC motor controller manufactured by ONSEMI is used.

4.4.2.6 Protection circuit

The one or more protection circuits 4250 may comprise electrical protection circuits, temperature and/or pressure safety circuits in accordance with the present techniques.

4.4.2.7 Memory

In accordance with one form of the present technique, RPT device 4000 includes a memory 4260, such as a non-volatile memory. In some forms, memory 4260 may comprise battery powered static RAM. In some forms, memory 4260 may comprise volatile RAM.

Memory 4260 may be located on PCBA 4202. The memory 4260 may be in the form of EEPROM or NAND flash memory.

Additionally, RPT device 4000 includes a removable form of memory 4260, such as a memory card manufactured according to the Secure Digital (SD) standard.

In one form of the present technology, the memory 4260 acts as a non-transitory computer-readable storage medium on which are stored computer program instructions that express one or more methods described herein, such as one or more algorithms 4300.

4.4.2.8 Data communication system

In one form of the present technology, a data communication interface 4280 is provided, and the data communication interface 4280 is connected to a central controller 4230 or other processor or control system (e.g., a computer system, exercise device, or other system) depending on the system. The data communication interface 4280 may be connected to a remote external communication network 4282 and/or a local external communication network 4284. The remote external communication network 4282 may be connected to the remote external device 4286. The local external communication network 4284 may be connected to the local external device 4288.

In one form, the data communication interface 4280 is part of the central controller 4230. In another form, the data communication interface 4280 is separate from the central controller 4230 and may comprise an integrated circuit or processor.

In one form, the remote external communication network 4282 is the internet. Data communication interface 4280 may connect to the internet using wired communication (e.g., via ethernet or fiber optic) or a wireless protocol (e.g., CDMA, GSM, LTE).

In one form, the local external communication network 4284 utilizes one or more communication standards, such as bluetooth or a consumer extrafiber protocol.

In one form, the remote external device 4286 is one or more computers, such as a networked computer cluster. In one form, the remote external device 4286 may be a virtual computer rather than a physical computer. In either case, such remote external device 4286 may be accessed by a suitably authorized person (such as a clinician).

The local external device 4288 may be a personal computer, a mobile phone, a tablet computer, or a remote control.

In some forms of the present technology, central controller 4230 will record usage data 9045 in memory 4260, which may contain output from treatment device controller 4240, a computer system, exercise equipment, or other systems. Usage data 9045 may contain several pieces or portions of data, including (1) date and time stamp, (2) start and stop time of use, (3) time of use for a period of time, (4) date and time of turning on and off RPT device 4000, computer system, exercise equipment or other system, and (5) treatment or other settings and sensor data, including readings from sensor 4270, including flow 4274, pressure 4272, speed 4276 (which in the case of an exercise machine may be the speed or intensity of exercise (e.g., stepper speed)).

In some forms of the present technology, usage data 9045 will be stored in local memory 4260. The usage data 9045 may also be transmitted to the remote external device 4286 or the local external device 4288 through the network via the data communication interface 4280.

In some examples, the central controller 4230 will send the usage data 9085 through the data communication interface 4280 one hour, two hours, three hours, or other suitable time range after the treatment period or other period (e.g., exercise period, driving period, website usage period) has ended. In other examples, usage data 9045 will be sent weekly. In some examples, if not turned on for any period of use on a particular day, the controller 4230 may send the usage data 9045 via the data communication interface 4280 at 1:00 a.m. or other suitable time of the next day or weekend. The usage data 9045 will indicate that a particular day or period of time contains a non-usage day. In some examples, usage data 9045 may distinguish between (1) turning on and not using a device (e.g., RPT device 4000, not turning on the apparatus or system at all). In some examples, RPT device 4000 or other device or system will not send usage data 9045 until it is turned back on, and will determine the amount of days of non-use since the last period of time that controller 4230 identified and stored in memory 4260.

The data sent over the data communication interface 4280 may be raw data or may be pre-processed data to save bandwidth, particularly in areas of poor cellular signal or other remote external communication network 4282 bandwidth. For example, usage data 9045 may be preprocessed to output relevant features for usage reduction or termination prediction. This may include days of non-use, average use (e.g., hours), and standard deviation of use. The relevant features may then be processed by the remote external device 4286 or an algorithm 4300 on a server.

In some forms of the present technology, the full use prediction algorithm may reside in the local memory 4260 and the controller 4230 may process the use data 9045 to determine the likelihood that the patient 1000 or other user (depending on the application) is exiting or reducing the use of the device itself. In these cases, the controller 4230 may send a signal to send an output to the display 4294 and/or send data to a local or remote external device, for example if the percentage likelihood exceeds a threshold.

4.4.2.9 Output device including optional display, alarm

The output device 4290 according to the present technology may take the form of one or more of visual, audio and tactile units. The visual display 000170 may be a Liquid Crystal Display (LCD) or a Light Emitting Diode (LED) display.

4.4.2.9.1 Display driver

The driver 4292 is shown receiving characters, symbols, or images to be shown on the display 4294 as input and converting them into commands that cause the display 4294 to show the characters, symbols, or images.

4.4.2.9.2 Display

The display 4294 is configured to visually show characters, symbols, or images in response to commands received from the display driver 4292. For example, the display 4294 may be an eight segment display, in which case the display driver 4292 converts each character or symbol, such as the number "0", into eight logical signals indicating whether eight corresponding segments are to be activated to show a particular character or symbol.

In some examples, the display may include a touch screen or remote interface, such as a smart phone that receives user or patient 1000 input.

4.4.3 Algorithm

As described above, in some forms of the present technology, the central controller 4230 or other processor may be configured to implement one or more algorithms represented as computer programs stored in a non-transitory computer readable storage medium (such as memory 4260). Algorithms are typically grouped into groups called modules. These algorithms may include feature detection algorithms and machine learning algorithms for predicting the reduction or termination of therapy, use of exercise equipment, or use of participating in on-demand services, such as taxi services using mobile applications that may track use (e.g., user or employee-e.g., driver).

4.4.3.1 Pretreatment module

The preprocessing module 4310, in one form of the present technology, receives as input a signal from the transducer 4270 (e.g., flow sensor 4274 or pressure sensor 4272) and performs one or more process steps to calculate one or more output values to be used as input to another module (e.g., treatment engine module 4320).

In one form of the present technique, the output values include an interface pressure Pm, a respiratory flow rate Qr, and a leak flow rate Q1.

In various forms of the present technology, the preprocessing module 4310 contains one or more of the following algorithms, interface pressure estimation 4312, ventilation flow estimation 4314, leakage flow estimation 4316, and respiratory flow estimation 4318.

4.4.3.1.1 Interface pressure estimation

In one form of the present technique, the interface pressure estimation algorithm 4312 receives as inputs a signal from the pressure sensor 4272 indicative of the pressure in the pneumatic path near the outlet of the pneumatic block (device pressure Pd) and a signal from the flow sensor 4274 indicative of the flow of air leaving the RPT device 4000 (device flow Qd). The device flow Qd without any make-up gas 4180 may be used as the total flow Qt. The interface pressure algorithm 4312 estimates the pressure drop Δp through the air circuit 4170. The dependence of the pressure drop Δp on the total flow Qt may be modeled for a particular air circuit 4170 by the pressure drop characteristic Δp (Q). The interface pressure estimation algorithm 4312 provides as an output an estimated pressure Pm in the patient interface 3000 or 3800. The pressure Pm in the patient interface 3000 or 3800 may be estimated as the device pressure Pd minus the air circuit pressure drop Δp.

4.4.3.1.2 Ventilation flow estimation

In one form of the present technique, the ventilation flow estimation algorithm 4314 receives as input an estimated pressure Pm in the patient interface 3000 or 3800 from the interface pressure estimation algorithm 4312 and estimates a ventilation flow Qv of air from the vent 3400 in the patient interface 3000 or 3800. For a particular vent 3400 in use, the dependence of the vent flow rate Qv on the interface pressure Pm may be simulated by the vent characteristic Qv (Pm).

4.4.3.1.3 Leakage flow estimation

In one form of the present technique, the leakage flow estimation algorithm 4316 receives as inputs the total flow Qt and the ventilation flow Qv and provides as output an estimate of the leakage flow Q1. In one form, the leakage flow estimation algorithm estimates the leakage flow Q1 by calculating an average of the difference between the total flow Qt and the ventilation flow Qv over a sufficiently long period of time (e.g., about 10 seconds).

In one form, the leakage flow estimation algorithm 4316 receives as inputs the total flow Qt, the ventilation flow Qv, and the estimated pressure Pm in the patient interface 3000 or 3800, and provides as output the leakage flow Q1 by calculating a leakage conductance and determining the leakage flow Q1 as a function of the leakage conductance and the pressure Pm. The leak conductance is calculated as the quotient of the low-pass filtered non-ventilation flow equal to the difference between the total flow Qt and the ventilation flow Qv, and the pressure square root Pm of the low-pass filter, where the low-pass filter time constant has a value long enough to contain several respiratory cycles, for example about 10 seconds. The leakage flow Q1 may be estimated as the product of the leakage conductance and a function of the pressure Pm.

4.4.3.1.4 Respiratory flow estimation

In one form of the present technique, the respiratory flow estimation algorithm 4318 receives as inputs the total flow Qt, the ventilation flow Qv, and the leakage flow Q1, and estimates the respiratory flow Qr of air to the patient by subtracting the ventilation flow Qv and the leakage flow Q1 from the total flow Qt.

4.4.3.2 Treatment engine module

In one form of the present technique, the therapy engine module 4320 receives as input one or more of the pressure Pm in the patient interface 3000 or 3800 and the respiratory flow Qr of air to the patient, and provides as output one or more therapy parameters.

In one form of the present technique, the treatment parameter is treatment pressure Pt.

In one form of the present technique, the therapeutic parameter is one or more of a pressure change amplitude, a base pressure, and a target ventilation.

In various forms, the treatment engine module 4320 contains one or more of the following algorithms, phase determination 4321, waveform determination 4322, ventilation determination 4323, inspiratory flow limitation determination 4324, apnea/hypopnea determination 4325, snore determination 4326, airway patency determination 4327, target ventilation determination 4328, and treatment parameter determination 4329.

4.4.3.2.1 Phase determination

In one form of the present technique, the RPT device 4000 does not determine phase.

In one form of the present technique, the phase determination algorithm 4321 receives as input a signal indicative of respiratory flow Qr and provides as output a phase Φ of the current respiratory cycle of the patient 1000.

In some forms, known as discrete phase measurements, the phase output Φ is a discrete variable. When the onset of spontaneous inhalation and exhalation is detected, respectively, one embodiment of the discrete phase measurement provides a two-valued phase output Φ having inhalation or exhalation values, e.g., values represented as 0 and 0.5 revolutions, respectively. The "trigger" and "cycle" effectively perform discrete phase-determined RPT devices 4000 because the trigger point and the cycle point are the moments in time when the phase changes from expiration to inspiration and from inspiration to expiration, respectively. In one embodiment of the two-value phase determination, the phase output Φ is determined to have a discrete value of 0 (thereby "triggering" the RPT device 4000) when the respiratory flow rate Qr has a value that exceeds a positive threshold, and the phase output Φ is determined to have a discrete value of 0.5 revolutions (thereby "cycling" the RPT device 4000) when the respiratory flow rate Qr has a value that is more negative than a negative threshold. The inspiration time Ti and expiration time Te may be estimated as typical values over a number of breathing cycles where the phase Φ is equal to 0 (indicating inspiration) and 0.5 (indicating expiration), respectively.

Another embodiment of the discrete phase measurement provides a three-value phase output Φ having values for one of inspiration, mid-inspiration pause, and expiration.

In other forms, known as continuous phase determination, the phase output Φ is a continuous variable, e.g., varying from 0 to 1 revolution or 0 to 2 radians. The RPT device 4000 performing the continuous phase assay may trigger and cycle when the continuous phase reaches 0 and 0.5 revolutions, respectively. In one embodiment of continuous phase determination, the continuous phase value Φ is determined using fuzzy logic analysis of the respiratory flow rate Qr. The successive values of the phases determined in this implementation are commonly referred to as "fuzzy phases". In one embodiment of the fuzzy phase determination algorithm 4321, the following rule is applied to the respiratory flow Qr:

1. if the respiratory flow is zero and increases rapidly, the phase is 0 revolutions.

2. If the respiratory flow is large positive and stable, the phase is 0.25 revolutions.

3. If the respiratory flow is zero and drops rapidly, the phase is 0.5 revolutions.

4. If the respiratory flow is negative and stable, the phase is 0.75 revolutions.

5. If the respiratory flow is zero and stable and the absolute value of the 5 second low pass filter of the respiratory flow is large, the phase is 0.9 revolutions.

6. If the respiratory flow is positive, the phase is exhalation, then the phase is 0 revolutions.

7. If the respiratory flow is negative and the phase is inspiratory, the phase is 0.5 revolutions.

8. If the absolute value of the 5 second low pass filter of the respiratory flow is large, the phase is increased at a steady rate equal to the patient respiratory rate, low pass filtered with a time constant of 20 seconds.

The output of each rule may be represented as a vector whose phase is the result of the rule and whose magnitude is the degree of ambiguity for which the rule is true. The degree of blurring of the respiratory flow "large", "stable", etc. is determined by means of a suitable membership function. The results of the rules are expressed as vectors and then combined by some function such as centroid. In such a combination, the rules may be weighted equally or weighted differently.

In another embodiment of continuous phase measurement, as described above, the phase Φ, the inspiration time Ti and the expiration time Te are first estimated discretely from the respiratory flow Qr. The continuous phase Φ at any instant can be determined as half the proportion of the inspiration time Ti that has elapsed since the previous trigger instant, or 0.5 revolutions plus half the proportion of the expiration time Te that has elapsed since the previous cycle instant (whichever instant is closer).

4.4.3.2.2 Waveform measurement

In one form of the present technique, the therapy parameter determination algorithm 4329 provides a substantially constant therapy pressure throughout the patient's respiratory cycle.

In other forms of the present technique, the therapy control module 4330 controls the pressure generator 4140 to provide a therapy pressure Pt that varies as a function of the phase Φ of the patient's respiratory cycle according to the waveform template pi (Φ).

In one form of the present technique, the waveform measurement algorithm 4322 provides a waveform template pi (Φ) having a value in the range of [0,1] over the domain of phase values Φ provided by the phase measurement algorithm 4321 for use by the treatment parameter measurement algorithm 4329.

In one form, suitable for discrete or continuous value phases, the waveform template pi (Φ) is a square wave template having a value of 1 for phase values up to and including 0.5 revolutions and a value of 0 for phase values above 0.5 revolutions. In one form, for continuous value phase, the waveform template pi (Φ) contains two smoothly curved portions, i.e., a smooth curve (e.g., raised cosine) rising from 0 to 1 for phase values up to 0.5 revolutions and a smooth curve (e.g., exponential) falling from 1 to 0 for phase values above 0.5 revolutions. In one form, the waveform template n (Φ) is based on a square wave, applicable to continuous value phases, but has a smooth rise from 0 to 1 for phase values up to a "rise time" of less than 0.5 revolutions, and a smooth fall from 1 to 0 for phase values within a "fall time" after 0.5 revolutions, with a "fall time" of less than 0.5 revolutions.

In some forms of the present technique, waveform measurement algorithm 4322 selects waveform template pi (Φ) from a library of waveform templates according to the settings of the RPT device. Each waveform template pi (Φ) in the library may be provided as a lookup table of values for the phase values. In other forms, the waveform determination algorithm 4322 calculates the "on-the-fly" waveform template pi (Φ) using a predetermined functional form that may be parameterized by one or more parameters (e.g., the time constant of the exponential curve portion). The parameters of the functional form may be predetermined or dependent on the current state of the patient 1000.

In some forms of the present technique, the waveform measurement algorithm 4322 is applied to discrete binary phases of inspiration (Φ=0 revolutions) or expiration (Φ=0.5 revolutions), and the waveform measurement algorithm 4322 calculates an "in-flight" waveform template ii (Φ, t) as a function of the discrete phase Φ and the time t measured since the last trigger time. In one such form, the waveform measurement algorithm 4322 calculates a two-part (inhalation and exhalation) waveform template, pi (t), as follows:

Wherein pi _i (t) and pi _e (t) are inhalation and exhalation portions of the waveform template pi (Φ, t). In one such form, the inspiratory portion pi _i (t) of the waveform template is a smooth rise from 0 to 1 parameterized by rise time, and the expiratory portion pi _e (t) of the waveform template is a smooth fall from 1 to 0 parameterized by fall time.

4.4.3.2.3 Ventilation measurement

In one form of the present technique, the ventilation determination algorithm 4323 receives an input of respiratory flow Qr and determines a measure Vent indicative of current patient ventilation.

In some embodiments, the ventilation determination algorithm 4323 determines a measure of ventilation that is an estimate of actual patient ventilation. One such implementation is to take half the absolute value of the respiratory flow Qr, optionally filtered by a low pass filter, such as a second order Bessel low pass filter with a corner frequency of 0.11 Hz.

In other embodiments, the ventilation determination algorithm 4323 determines a measure of ventilation that is approximately proportional to the actual patient ventilation. One such embodiment estimates the peak respiratory flow Qpeak over the inspiratory portion of the cycle. This process, and many other processes involving sampling the respiratory flow rate Qr, produce a measurement that is approximately proportional to ventilation, provided that the flow rate waveform shape is not very varied (herein, when the flow rate waveforms of the breaths normalized in time and amplitude are similar, the shapes of the two breaths are considered similar). Some simple examples include median of median respiratory flow, respiratory flow absolute value, and standard deviation of flow. Any linear combination of any sequential statistic of absolute value of respiratory flow using positive coefficients, even some of which are approximately proportional to ventilation. Another example is the average of the respiratory flow in the middle K ratio (in time) of the inspiratory portion, where 0< K <1. If the flow shape is constant, there is any large number of measurements that are precisely proportional to the ventilation.

4.4.3.2.4 Determination of inspiratory flow limitation

In one form of the present technique, the central controller 4230 executes an inhalation flow restriction measurement algorithm 4324 to determine the extent of inhalation flow restriction.

In one form, the inspiratory flow limitation determination algorithm 4324 receives as input the respiratory flow signal Qr and provides as output a measure of the extent to which the inspiratory portion of the breath exhibits inspiratory flow limitation.

In one form of the present technique, the inspiratory portion of each breath is identified by a zero crossing detector. An interpolator interpolates a plurality of evenly spaced points (e.g., sixty-five) representing points in time along an inspiratory flow-time curve for each breath. The curve described by the points is then scaled by a scalar to have a unit length (duration/period) and a unit area to remove the effects of varying respiration rate and depth. The scaled breath is then compared in a comparator with a pre-stored template representing a normal non-blocking breath, similar to the inspiratory portion of the breath shown in fig. 6A. Respiration that deviates from the template by more than a specified threshold (typically 1 proportional unit) at any time during inspiration, such as those determined by the test element due to cough, sigh, swallowing, and hiccup, is rejected. For data that is not rejected, a moving average of the first such zoom points is calculated by central controller 4230 for the previous several inhalation events. For the second such point, this is repeated on the same inhalation event, and so on. Thus, for example, sixty-five scaled data points are generated by the central controller 4230 and represent a moving average of the previous several inhalation events, e.g., three events. The moving average of the continuously updated values of these (e.g., 65) points is referred to hereinafter as "scaled flow", designated Qs (t). Alternatively, a single inhalation event may be used instead of a moving average.

From the scaled flow, two form factors can be calculated that are relevant to the determination of partial obstruction.

Shape factor 1 is the ratio of the average of the intermediate (e.g., 32) scaled flow points to the ensemble average (e.g., 65) scaled flow points. In the case where the ratio exceeds a unit, respiration will be considered normal. In the case where the ratio is unit or less, respiration will be blocked. A ratio of about 1.17 is considered to be the threshold between partially occluded and non-occluded breathing and is equal to the degree of occlusion that allows adequate oxygenation to be maintained in a typical patient.

The shape factor 2 is calculated as the RMS deviation of the unit scaled flow taken from the intermediate (e.g., 32) points. An RMS deviation of about 0.2 units is considered normal. Take an RMS deviation of 0 as a fully current-limiting breath. The closer the RMS deviation is to zero, the more the breath is restricted.

Form factors 1 and 2 may be used as alternatives or in combination. In other forms of the present technique, the number of sampling points, breaths, and midpoints may be different than those described above. Furthermore, the threshold values may be different from the described threshold values.

4.4.3.2.5 Determination of apneas and hypopneas

In one form of the present technique, the central controller 4230 executes an apnea/hypopnea determination algorithm 4325 for determining the presence of an apnea and/or hypopnea.

In one form, the apnea/hypopnea measurement algorithm 4325 receives as input a respiratory flow signal Qr and provides as output a flag indicating that an apnea or hypopnea has been detected.

In one form, an apnea will be considered to have been detected when the function of respiratory flow Qr falls below the flow threshold for a predetermined period of time. The function may determine peak flow, relatively short term average flow, or flow between relatively short term average and peak flow, such as RMS flow. The flow threshold may be a relatively long-term measure of flow.

In one form, a hypopnea will be considered to have been detected when the function of respiratory flow Qr falls below the second flow threshold within a predetermined period of time. The function may determine a peak flow, a relatively short-term average flow, or a flow between a relatively short-term average flow and a peak flow, such as an RMS flow. The second flow threshold may be a relatively long-term measure of flow. The second flow threshold is greater than the flow threshold for detecting apneas.

4.4.3.2.6 Determination of snoring

In one form of the present technique, the central controller 4230 executes one or more snore determining algorithms 4326 for determining the degree of snoring.

In one form, the snore determining algorithm 4326 receives the respiratory flow signal Qr as an input and provides a measure of the degree of snoring present as an output.

The snore determining algorithm 4326 can include the step of determining the intensity of the flow signal in the range of 30-300 Hz. In addition, the snoring determination algorithm 4326 can include the step of filtering the respiratory flow signal Qr to reduce background noise (e.g., sound from the airflow in the system of the blower).

4.4.3.2.7 Determination of airway patency

In one form of the present technique, the central controller 4230 executes one or more airway patency determination algorithms 4327 for determining an airway patency.

In one form, airway patency determination algorithm 4327 receives as input respiratory flow signal Qr and determines signal power in a frequency range of about 0.75HZ and about 3 HZ. The presence of peaks in this frequency range is used to indicate an open airway. The absence of a peak is considered an indication of closed airways.

In one form, the frequency range over which the peak is sought is the frequency of the small forced oscillation in the process pressure Pt. In one embodiment, the forced oscillation has a frequency of 2HZ and an amplitude of about 1cmH ₂ O.

In one form, the airway patency determination algorithm 4327 receives the respiratory flow signal Qr as an input and determines the presence or absence of a cardiogenic signal. The lack of cardiogenic signals is considered an indication of closed airways.

4.4.3.2.8 Determination of target ventilation

In one form of the present technique, the central controller 4230 takes as input the current ventilation Vent and executes one or more target ventilation determination algorithms 4328 for determining a target value Vtgt of ventilation.

In some forms of the present technique, there is no target ventilation measurement algorithm 4328, and the target value Vtgt is predetermined, such as by hard coding during configuration of the RPT device 4000 or by manual input via the input device 4220.

In other forms of the present technique, such as Adaptive Servo Ventilation (ASV), the target ventilation determination algorithm 4328 calculates the target value Vtgt from a value Vtyp representing the typical recent ventilation of the patient.

In some forms of adaptive servo ventilation, the target ventilation Vtgt is calculated as a high proportion of the typical most recent ventilation Vtyp, but less than the typical most recent ventilation Vtyp. The high proportion in these forms may be in the range of (80%, 100%) or (85%, 95%) or (87%, 92%).

In other forms of adaptive servo ventilation, the target ventilation Vtgt is calculated to be slightly greater than an integer multiple of the typical recent ventilation Vtyp.

A typical recent ventilation Vtyp is a value around which the distribution of the measure of current ventilation Vent over some predetermined time scale at multiple times tends to aggregate, i.e., a measure of the central tendency of the measure of current ventilation over recent history. In one embodiment of the target ventilation determination algorithm 4328, the recent history is on the order of minutes, but in any event should be longer than the time scale of the Cheyne-Stokes waxing and waxing cycles. The target ventilation measurement algorithm 4328 may use any of a variety of well-known central trend metrics to determine a typical most recent ventilation Vtyp from a measure of the current ventilation Vent. One such measure is to output a low pass filter on the current ventilation measurement with a time constant equal to one hundred seconds.

4.4.3.2.9 Determination of treatment parameters

In some forms of the present technique, the central controller 4230 executes one or more therapy parameter determination algorithms 4329 for determining one or more therapy parameters using values returned by one or more other algorithms in the therapy engine module 4320. This may include an algorithm 4300 for changing treatment parameters if the patient 1000 is marked for reducing or terminating treatment.

In one form of the present technique, the treatment parameter is instantaneous treatment pressure Pt. In one implementation of this form, the therapy parameter determination algorithm 4329 uses the following equation to determine the therapy pressure Pt:

Pt=AΠ(Φ,t)+P₀ (1)

wherein:

a is the amplitude of the signal and,

Pi (Φ, t) is the waveform template value (in the range of 0 to 1) at the current value Φ of phase and time t, and

P ₀ is the base pressure.

If the waveform measurement algorithm 4322 provides the waveform template pi (Φ, t) as a lookup table of values pi indexed by the phase Φ, the treatment parameter measurement algorithm 4329 applies equation (1) by locating the most recent lookup table entry to the current value Φ of the phase returned by the phase measurement algorithm 4321, or by interpolating between two entries spanning the current value Φ of the phase.

The values of amplitude a and base pressure P ₀ may be set by the therapy parameter determination algorithm 4329 according to the selected respiratory pressure therapy mode in the manner described below.

4.4.3.3 Treatment control module

The therapy control module 4330, in accordance with one aspect of the present technique, receives as input therapy parameters from the therapy parameter determination algorithm 4329 of the therapy engine module 4320 and controls the pressure generator 4140 to deliver an air flow in accordance with the therapy parameters.

In one form of the present technique, the therapy parameter is a therapy pressure Pt, and the therapy control module 4330 controls the pressure generator 4140 to deliver an air flow having an interface pressure Pm at the patient interface 3000 or 3800 equal to the therapy pressure Pt.

4.4.3.4 Detect a fault condition

In one form of the present technique, the central controller 4230 performs one or more methods 4340 for detecting a fault condition. The fault condition detected by the one or more methods 4340 may include at least one of:

Failure of power supply (no or insufficient power supply)

Converter fault detection

Failing to detect the presence of a component

Operating parameters outside the recommended range (e.g., pressure, flow, temperature, paO ₂).

The test alarm cannot generate a detectable alarm signal.

Upon detection of a fault condition, the corresponding algorithm 4340 signals the presence of a fault by one or more of:

Activating audible, visual and/or dynamic (e.g. vibration) alarms

Sending a message to an external device

Logging of events

4.5 Humidifier

4.5.1 Overview of humidifier

In one form of the present technique, a humidifier 5000 (e.g., as shown in fig. 5A) is provided to vary the absolute humidity of the air or gas for delivery to the patient relative to ambient air. Generally, humidifier 5000 is used to increase absolute humidity and increase the temperature of the air flow (relative to ambient air) prior to delivery to the patient's airway.

The humidifier 5000 may include a humidifier reservoir 5110, a humidifier inlet 5002 for receiving an air stream, and a humidifier outlet 5004 for delivering the humidified air stream. In some forms, as shown in fig. 5A and 5B, the inlet and outlet of the humidifier reservoir 5110 may be a humidifier inlet 5002 and a humidifier outlet 5004, respectively. The humidifier 5000 may also include a humidifier base 5006, which may be adapted to receive the humidifier reservoir 5110 and include a heating element 5240.

4.6 Respiratory waveform

Fig. 6A shows a model of a typical breathing waveform of a person while sleeping. The horizontal axis is time and the vertical axis is respiratory flow. While parameter values may vary, typical breaths may have approximations of tidal volume Vt 0.5L, inspiration time Ti 1.6s, peak inspiratory flow Qpeak 0.4L/s, expiration time Te 2.4s, peak expiratory flow Qpeak-0.5L/s. The total duration Ttot of the breath is about 4s. The person typically breathes at a rate of about 15 Breaths Per Minute (BPM) with a ventilation outlet of about 7.5L/min. The typical duty cycle, ti to Ttot ratio, is about 40%.

Fig. 6B shows selected polysomnography channels (pulse oximetry, flow rate, chest movement, and abdominal movement) of a patient during non-REM sleep breaths during a period of about ninety seconds under normal conditions, with about 34 breaths being treated with automated PAP therapy and an interface pressure of about 11cmH ₂ O. The top channel shows pulse oximetry (oxygen saturation or SpO ₂) with a range of saturation from 90% to 99% in the vertical direction. The patient maintains about 95% saturation for the indicated period of time. The second channel showed a quantitative respiratory airflow, graduated from-1 LPS to +1LPS in the vertical direction, with positive inspiration. Chest and abdomen movements are shown in the third and fourth channels.

4.7 Screening, diagnostic, monitoring System

4.7.1 Polysomnography

Fig. 7A shows a patient 1000 undergoing Polysomnography (PSG). The PSG system includes a headbox 2000 that receives and records signals from EOG electrode 2015, eeg electrode 2020, ecg electrode 2025, submandibular EMG electrode 2030, snore sensor 2035, respiratory inductive plethysmogram (respiratory effort sensor) 2040 on the chest, respiratory inductive plethysmogram (respiratory effort sensor) 2045 on the abdomen, oronasal cannula 2050 with oral thermistor, photoplethysmogram (pulse oximeter) 2055, and body position sensor 2060. The electrical signal is referenced to a ground electrode (ISOG) 2010 centered on the forehead.

4.7.2 Non-invasive monitoring System

Fig. 7B illustrates one example of a monitoring device 7100 for monitoring respiration of a sleeping patient 1000. The monitoring device 7100 includes a non-contact motion sensor that is generally directed toward the patient 1000. The motion sensor is configured to generate one or more signals representative of the body motion of the patient 1000 from which signals representative of the respiratory motion of the patient can be obtained. In other examples, the system may contain environmental and other acoustic sensors to sense noise of the environment, vents, and patient 1000.

4.7.3 Respiratory multispectral method

Respiratory multi-Recording (RPG) is a term for a simplified form of PSG that does not have an electrical signal (EOG, EEG, EMG), snoring, or body position sensor. The RPG contains at least chest motion signals from a respiratory sensing plethysmogram (motion sensor) on the chest strap, such as motion sensor 2040, nasal pressure signals sensed via nasal cannula, and oxygen saturation signals from a pulse oximeter (e.g., pulse oximeter 2055). These three RPG signals or channels are received by an RPG headbox similar to the PSG headbox 2000.

In some configurations, the nasal pressure signal is a satisfactory proxy for the nasal flow signal generated by the flow transducer in-line with the sealed nasal mask, as the nasal pressure signal is comparable in shape to the nasal flow signal. If the patient's mouth remains closed, i.e., there is no mouth leak, then nasal flow is again equal to respiratory flow.

Fig. 7C is a block diagram illustrating a screening/diagnostic/monitoring device 7200 that may be used to implement an RPG headbox in an RPG screening/diagnostic/monitoring system. The screening/diagnostic/monitoring device 7200 receives the three RPG channels (signal indicative of chest movement, signal indicative of nasal flow, and signal indicative of oxygen saturation) at the data input interface 7260. The screening/diagnostic/monitoring device 7200 further includes a processor 7210 configured to execute coded instructions. The screening/diagnostic/monitoring device 7200 also includes a non-transitory computer readable memory/storage medium 7230.

The memory 7230 may be an internal memory of the screening/diagnostic/monitoring device 7200, such as RAM, flash memory, or ROM. In some implementations, the memory 7230 can also be a removable or external memory linked to the screening/diagnostic/monitoring device 7200, such as, for example, an SD card, a server, a USB flash drive, or an optical disk. In other implementations, the memory 7230 may be a combination of external and internal memory. The memory 7230 includes stored data 7240 and processor control instructions (code) 7250 adapted to configure the processor 7210 to perform particular tasks. The storage data 7240 may include RPG channel data received by the data input interface 7260, as well as other data provided as part of the application. Processor control instructions 7250 may also be provided as part of an application. The processor 7210 is configured to read the code 7250 from the memory 7230 and execute the encoded instructions. In particular, code 7250 may include instructions adapted to configure processor 7210 to perform a method of processing RPG channel data provided by interface 7260. One such method may be to store RPG channel data as data 7240 in memory 7230. Another such method may be to analyze the stored RPG data to extract features. The processor 7210 can store the results of such analysis as data 7240 in the memory 7230.

The screening/diagnostic/monitoring device 7200 can also include a communication interface 7220. The code 7250 may contain instructions configured to allow the processor 7210 to communicate with an external computing device (not shown) via the communication interface 7220. The communication mode may be wired or wireless. In one such embodiment, the processor 7210 may transmit the stored RPG channel data from the data 7240 to a remote computing device. In such an embodiment, the remote computing device may be configured to analyze the received RPG data to extract the features. In another such embodiment, the processor 7210 may transmit the analysis results from the data 7240 to a remote computing device.

Alternatively, if the memory 7230 is removable from the screening/diagnostic/monitoring device 7200, the remote computing device may be configured to connect to the removable memory 7230. In such an embodiment, the remote computing device may be configured to analyze the RPG data retrieved from the removable memory 7230 to extract features.

4.8 Respiratory treatment modes

The RPT device 4000 may implement various respiratory therapy modes.

4.8.1 CPAP therapy

In some embodiments of respiratory pressure therapy, the central controller 4230 sets the therapy pressure Pt according to therapy pressure equation (1) as part of the therapy parameter determination algorithm 4329. In one such embodiment, the amplitude a is equal to zero, so the therapeutic pressure Pt (which represents the target value achieved by the interface pressure Pm at the current moment) is equal to zero throughout the respiratory cycle. Such embodiments are generally categorized under the heading of CPAP therapy. In these embodiments, the therapy engine module 4320 is not required to determine the phase Φ or the waveform template pi (Φ).

In CPAP therapy, the base pressure P ₀ may be a constant value that is hard-coded or manually input to the RPT device 4000. Alternatively, the central controller 4230 may repeatedly calculate the base pressure P ₀ from indicators or measures of sleep disordered breathing (such as one or more of flow restriction, apneas, hypopneas, patency, and snoring) returned by the corresponding algorithm in the therapy engine module 4320. This alternative approach is sometimes referred to as APAP therapy.

Fig. 4E is a flow chart illustrating a method 4500 performed by the central controller 4230 to continuously calculate the base pressure P ₀ as part of an APAP therapy embodiment of the therapy parameter determination algorithm 4329 when the pressure support a is equal to zero.

Method 4500 begins at step 4520 and at step 4520 central controller 4230 compares the measure of the presence of apnea/hypopnea to a first threshold and determines if the measure of the presence of apnea/hypopnea has exceeded the first threshold for a predetermined period of time, indicating that apnea/hypopnea is occurring. If so, the method 4500 proceeds to step 4540, otherwise, the method 4500 proceeds to step 4530. In step 4540, central controller 4230 compares the measurement of airway patency to a second threshold. If the measure of airway patency exceeds the second threshold, indicating airway patency, then the detected apnea/hypopnea is considered central and method 4500 proceeds to step 4560, otherwise, the apnea/hypopnea is considered obstructive and method 4500 proceeds to step 4550.

In step 4530, the central controller 4230 compares the measured value of the flow restriction with a third threshold. If the measure of flow restriction exceeds the third threshold, indicating that the inspiratory flow is restricted, then the method 4500 proceeds to step 4550, otherwise, the method 4500 proceeds to step 4560.

In step 4550, if the resulting process pressure Pt does not exceed the maximum process pressure P _max, the central controller 4230 increases the base pressure P0 by a predetermined pressure increment P. In one embodiment, the predetermined pressure increase P and the maximum therapeutic pressure P _max are 1cmH20 and 25cmH20, respectively. In other embodiments, the pressure increase P may be as low as 0.1cmH ₂ O and as high as 3cmH ₂ O, or as low as 0.5cmH ₂ O and as high as 2cmH ₂ O. In other embodiments, the maximum process pressure P _max may be as low as 15cmH ₂ O and as high as 35cmH ₂ O, or as low as 20cmH ₂ O and as high as 30cmH ₂ O. Method 4500 may then return to step 4520.

At step 4560, the central controller 4230 decreases the base pressure P ₀ by an amount as long as the reduced base pressure P ₀ does not drop below the minimum process pressure P _min. Method 4500 may then return to step 4520. In one embodiment, the decrease is proportional to the value of P ₀-P_min, such that the decrease in P ₀ to the minimum process pressure P _min is exponential without any detected events. In one embodiment, the proportionality constant is set such that the exponentially decreasing time constant for P ₀ is 60 minutes and the minimum process pressure P _min is 4cmH ₂ O. in other embodiments, the time constant may be as low as 1 minute and as high as 300 minutes, or as low as 5 minutes and as high as 180 minutes. In other embodiments, the minimum process pressure P _min may be as low as 0cmH ₂ O and as high as 8cmH ₂ O, or as low as 2cmH ₂ O and as high as 6cmH ₂ O. Alternatively, the decrease amount of P ₀ can be preset so that the decrease amount of P ₀ to the minimum process pressure P _min is linear without any event being detected.

4.8.2 Double level treatment

In other embodiments of this form of the present technology, the value of amplitude a in equation (1) may be positive. Such an embodiment is referred to as bi-level therapy because the therapy parameter determination algorithm 4329 oscillates the therapy pressure Pt between two values or levels synchronized with the spontaneous respiratory effort of the patient 1000 when determining the therapy pressure Pt using equation (1) with a positive amplitude a. That is, based on the above-described typical waveform template pi (Φ, t), the therapy parameter determination algorithm 4329 increases the therapy pressure Pt to P ₀ +a (referred to as IPAP) at the beginning of expiration or during inspiration, and decreases the therapy pressure Pt to the base pressure P ₀ (referred to as EPAP) at the beginning of expiration or during expiration.

In some forms of bi-level therapy, IPAP is the same target therapeutic pressure as the therapeutic pressure in the CPAP treatment mode, and EPAP is the IPAP minus the amplitude A, which has a "small" value (a few cmH ₂ O), sometimes referred to as Expiratory Pressure Relief (EPR). This form is sometimes referred to as CPAP therapy with EPR, which is generally considered more comfortable than direct CPAP therapy. In CPAP therapy using EPR, one or both of the IPAP and the EPAP may be a constant value hard-coded or manually input to the RPT device 4000. Alternatively, the treatment parameter determination algorithm 4329 may repeatedly calculate IPAP and/or EPAP during CPAP and EPR. In this alternative, the therapy parameter determination algorithm 4329 repeatedly calculates EPAP and/or IPAP from the indicators or measurements of sleep disordered breathing returned by the corresponding algorithm in the therapy engine module 4320 in a manner similar to the calculation of the base pressure P ₀ in the APAP therapy described above.

In other forms of bi-level therapy, the amplitude a is large enough that the RPT device 4000 completes part or all of the respiratory effort of the patient 1000. In this form, known as pressure support ventilation therapy, amplitude a is known as pressure support or swing. In pressure support ventilation therapy, IPAP is the base pressure P ₀ to pressurize the pressure support A and EPAP is the base pressure P ₀.

In some forms of pressure support ventilation therapy, known as fixed pressure support ventilation therapy, pressure support a is fixed at a predetermined value, such as 10cmH ₂ O. The predetermined pressure support value is a setting of the RPT device 4000 and may be set, for example, by hard coding during configuration of the RPT device 4000 or by manual input via the input device 4220.

In other forms of pressure support ventilation therapy, broadly referred to as servo ventilation, the therapy parameter determination algorithm 4329 takes as input some currently measured or estimated parameter of the respiratory cycle (e.g., the current measured value Vent of ventilation) and a target value of that respiratory parameter (e.g., the target value Vtgt of ventilation), and repeatedly adjusts the parameters of equation (1) to bring the current measured value of respiratory parameter to the target value. In one form of servo ventilation known as Adaptive Servo Ventilation (ASV), which has been used to treat CSR, the respiratory parameter is ventilation, and the target ventilation value Vtgt is calculated by the target ventilation value determination algorithm 4328 from the typical most recent ventilation value Vtyp, as described above.

In some forms of servo ventilation, the therapy parameter determination algorithm 4329 applies a control method to repeatedly calculate the pressure support a in order to bring the current measured value of the respiratory parameter to the target value. One such control method is Proportional Integral (PI) control. In one embodiment of PI control, applicable to ASV mode, where the target ventilation Vtgt is set to be slightly less than the typical recent ventilation Vtyp, the pressure support a is repeatedly calculated as:

A=G∫(Vent-Vtgt)dt (2)

Where G is the gain of the PI control. A larger gain value G may result in positive feedback in the therapy engine module 4320. A smaller gain G value may allow some residual untreated CSR or central sleep apnea. In some embodiments, the gain G is fixed to a predetermined value, such as-0.4 cmH ₂ O/(L/min)/sec. Alternatively, the gain G may vary between treatment periods, starting to be smaller and increasing from one period to another until a value is reached that substantially eliminates CSR. Conventional means for retrospectively analyzing parameters of a treatment session to assess the severity of CSR during the treatment session may be employed in such embodiments. In other embodiments, the gain G may vary according to the difference between the current ventilation measure Vent and the target ventilation Vtgt.

Other servo-ventilation control methods that may be applied by the therapy parameter determination algorithm 4329 include proportional (P), proportional-derivative (PD), and proportional-integral-derivative (PID).

The pressure support limits a _min and a _max are settings of the RPT device 4000, for example by hard coding during configuration of the RPT device 4000 or by manual input through the input device 4220.

In pressure support ventilation therapy mode, EPAP is base pressure P ₀. As with the base pressure P ₀ in CPAP therapy, the EPAP may be a constant value specified or determined during titration. Such a constant EPAP may be set, for example, by hard coding during configuration of RPT device 4000 or by manual input via input device 4220. This alternative approach is sometimes referred to as fixed EPAP pressure support ventilation therapy. Titration of EPAP for a given patient may be performed by a clinician during a titration period with the aid of a PSG in order to prevent obstructive apneas, thereby maintaining an open airway for pressure support ventilation therapy in a manner similar to titration of base pressure P ₀ in constant CPAP therapy.

Alternatively, the therapy parameter determination algorithm 4329 may repeatedly calculate the base pressure P ₀ during the pressure support ventilation therapy. In such embodiments, the therapy parameter determination algorithm 4329 repeatedly calculates EPAP, such as one or more of flow restriction, apnea, hypopnea, patency, and snoring, from indicators or metrics of sleep disordered breathing returned by the corresponding algorithm in the therapy engine module 4320. Because continuous calculation of EPAP is similar to manual adjustment of EPAP by a clinician during EPAP titration, this process is sometimes also referred to as automatic titration of EPAP, and the treatment mode is referred to as automatic titration EPAP pressure support ventilation therapy or automatic EPAP pressure support ventilation therapy.

4.8.3 High flow therapy

In other forms of respiratory therapy, the pressure of the air stream is uncontrolled as it is used for respiratory pressure therapy. Instead, the central controller 4230 controls the pressure generator 4140 to deliver an air flow whose device flow Qd is controlled to the therapeutic or target flow Qtgt. Such forms are generally categorized under the heading of flow therapy. In flow therapy, the therapeutic flow Qtgt may be a constant value hard coded or manually entered into the RPT device 4000. If the therapeutic flow Qtgt is sufficient to exceed the patient's peak inspiratory flow, the therapy is commonly referred to as High Flow Therapy (HFT). Alternatively, the therapeutic flow may be a curve Qtgt (t) that varies with the respiratory cycle.

4.9 Data Transmission and Collection

A connection device such as RPT 4000 in fig. 4A or other apparatus such as an exercise machine (treadmill or stationary exercise bicycle) is capable of storing and transmitting different levels of data. For example, the central controller 4230 in fig. 4C or a suitable device processor may send data to the external source 4286. Such data may include data collected by sensors of RPT 4000, such as flow rate sensor 4272 or pressure sensor 4272, data collected by exercise equipment sensors, such as speed and length of exercise, data collected by a computer system, such as frequency and type of user interaction with a user interface, data generated by algorithms of preprocessing module 4310 or other algorithms, or data generated by algorithms of treatment engine module 4320. Such data may be combined for analysis by algorithms that produce more data.

Fig. 8A illustrates a block diagram showing one embodiment 7000 of a system in accordance with the present technology, which may be an RPT system, an exercise system, a computer hardware and software usage system, an on-demand service for employee and customer use, a digital health online service, or other suitable service or system. Thus, in addition to the RPT system, the system and apparatus may also comprise:

exercise and healthcare system

Electronic exercise equipment containing output usage data of the treadmill;

stationary bicycle, digital counterweight;

fitness tracker, wearable such as detecting motion, exercise, and other factors, and

Other exercise and healthcare systems

Computer system, software and interface

Web sites and software programs tracking usage data, including healthcare software for monitoring compliance with CBT programs and other online or software programs;

Weight loss monitoring applications that monitor things like eating, exercise, food type, and others that can track usage and other data;

Computer hardware and software for coordinating on-demand services, such as on-demand taxi services, wherein the software and/or hardware can track usage data, and

Other software and services.

The system 7000 may include a device 4000 configured to provide respiratory pressure therapy to the patient 1000 or other services to the user, a data server 7010, and a computing device 7050 associated with the patient 1000 or user. Computing device 7050 may be placed with user 1000 and device 4000, such as an RPT device. In the embodiment 7000 shown in fig. 7A, the device 4000, the computing device 7050, and the data server 7010 are connected to a wide area network 7090, such as the internet, the cloud, or the internet.

The connection to the wide area network may be wired or wireless. The wide area network may be identified with the remote external communication network 4282 of fig. 4C, and the data server 7010 may be identified with the remote external device 4286 of fig. 4C. The computing device 7050 may be a personal computer, mobile phone, tablet computer, or other device, and may be incorporated into the various devices disclosed herein. The computing device 7050 may be configured to be interposed between a user (e.g., patient 1000) and the data server 7010 via a wide area network 7090. In one implementation, the intermediary is performed by a software application 7060 running on a computing device 7050. The user program 7060 may be a dedicated application called a "user application" that interacts with the complementary process hosted by the data server 7010. In another embodiment, the user program 7060 is a web browser that interacts with a website hosted by the data server 7010 via a secure portal. In yet another embodiment, the user program 7060 is an email client.

Fig. 8B contains a block diagram illustrating an alternative embodiment 7000B of a system in accordance with the present technique. In an alternative implementation 7000B, the device 4000 communicates with the computing device 7050 via a local (wired or wireless) communication protocol, such as a local network protocol (e.g., bluetooth). In alternative embodiment 7000B, the local network may be identified with local external communication network 4284 of fig. 4C, and user computing device 110 may be identified with local external device 4288 of fig. 4C. In an alternative embodiment 7000B, the user patient computing device 7050 is configured via the user program 7060 to be interposed between the user (e.g., patient 1000) and the data server 7010 on the wide area network 7090, and also interposed between the device 4000 and the data server 7010 on the wide area network 7090.

In the following, statements about the system 7000 may be understood as equally applicable to the alternative embodiment 7000B, unless explicitly stated otherwise.

The system 7000 may contain other devices (not shown) associated with a respective user or patient, which also has a respective associated computing device. In addition, the system 7000 may contain other monitoring or therapy devices that may interface with the controller 4230 or the user computing device 7050.

The apparatus 4000 may be configured to store data (e.g., the patient 1000) from each usage period delivered to the user in the memory 4260. For example, the treatment data for an RPT period includes the settings of the RPT device 4000 and treatment variable data representing one or more variables of the respiratory pressure treatment for the entire RPT period. In other examples, the data for the usage period may include speed or other intensity settings on the exercise equipment, length of the usage or exercise period, length of website usage, number of clicks, or other suitable usage and interaction data.

The apparatus 4000 may be configured to send data to a data server 7010. As described above, transmission of data is modulated based on different situations. In normal operation, only low resolution data is transmitted. The high resolution data may be transmitted in different situations, as will be explained below. The data server 7010 may receive data from the apparatus 4000 according to a "pull" model, whereby the apparatus 4000 transmits data in response to a query from the data server 7010. Alternatively, the data server 7010 may receive data according to a "push" model, whereby the apparatus 4000 sends the data to the data server 7010 as soon as possible after a period of time.

The data received from device 4000 may be stored and indexed by data server 7010 so as to be uniquely associated with device 4000 and thus distinguishable from data from any other device participating in system 7000.

In this example, the data server 7010 is configured to calculate different types of analytical data that are available to a clinician or system administrator. For example, usage data for each period may be determined from data received from the apparatus 4000. The usage data variable for a period of time contains summary statistics derived by conventional scoring means from variable data forming part of the data.

The usage data may include one or more of the following usage variables:

The time of use, i.e. the total duration of the segment;

an Apnea Hypopnea Index (AHI) for a period of time;

Average leakage flow rate of time period

Average mask pressure for a period of time;

the number of "sub-periods" in the RPT period, i.e., the number of intervals of RPT treatment between "mask on" and "mask off" events;

other statistical summaries of treatment variables, such as 95 th percentile pressure, median pressure, pressure value histogram;

average speed measured by running, cycling or other exercise device;

the length of the exercise session;

heart rate or other physiological indicators tracked by the wearable, for example during exercise or other use;

Repetition frequency;

Power (speed times weight)

● Accelerating;

Average exercise session of week;

calories burned;

the length of time the software program is used;

the number of mouse clicks per session;

the number of times a software program, RPT device, on-demand service, or other system is used per time period (e.g., day, week, month), and

Multiple session statistics such as average, median and variance of AHI from the start of RPT treatment, trend of exercise duration, calorie burn, speed or other metrics, and

● Others.

Other servers may be coupled to the network 7090 and retrieve data based on the "push" or "pull" model described above. For example, the data server 7100 operated by the payee may receive data for different purposes, such as determining compliance or predicting compliance for use in determining payment for treatment of the patient 1000 or other services of the user. The machine learning server 7200 may also receive data for learning or refining the baseline of abnormal situations requiring high resolution data, as follows. Alternatively, the machine learning server 7200 may learn an optimal response when an abnormal situation is detected, or learn correct prediction data to be included in the high-resolution data in response to some abnormal situation.

In an alternative embodiment, the apparatus 4000 calculates the usage variables from the data stored by the apparatus 4000 at the end of each period. The apparatus 4000 then sends the usage variables to the data server 7010 according to the "push" or "pull" model described above.

In another embodiment, the memory 4260 in which the device 4000 stores treatment/usage data for each session is in a removable form, such as an SD memory card. Removable memory 4260 may be removed from device 4000 and inserted into a card reader in communication with data server 7010. The treatment/usage data is then copied from the removable memory 4260 to the memory of the data server 7010.

In yet another embodiment, which is an alternative embodiment 7000B for a system, the device 4000 is configured to send treatment/usage data to the user computing device 7050 via a wireless communication protocol such as bluetooth as described above. The user computing device 7050 then sends the treatment/usage data to the data server 7010. The data server 7010 may receive therapy/usage data from the user computing device 7050 in accordance with a "pull" model whereby the user computing device 7050 transmits the therapy/usage data in response to a query from the data server 7010. Alternatively, the data server 7010 may receive the treatment/usage data in accordance with a "push" model whereby the user computing device 7050 sends the treatment/usage data to the data server 7010 as soon as it is available after the period of time.

In some implementations, the data server 7010 can perform some post-processing of the usage data, such as with one or more processors in communication with the data server 7010 or contained in the data server 7010. One example of such post-processing is determining whether the most recent period is a "compliance period". Some compliance rules specify the RPT device usage required during a compliance time period (such as 30 days) based on a minimum duration (e.g., four hours) of device usage per period of a certain minimum number of days (e.g., 21 days) during the compliance time period.

A period is considered compliant if its duration exceeds a minimum duration. Usage data post-processing may determine whether the most recent period is a compliance period by comparing the usage duration to a minimum duration from compliance rules. The result of this post-processing is compliance data, such as boolean compliance variables, that form part of the usage data. Another example of multi-session usage data is a compliance session count since the start of an RPT therapy or other type of session.

The data server 7010 may also be configured to receive data from the user computing devices 7050. This may include data entered by the user or patient 1000 into the user program 7060, or treatment/usage data in the alternative embodiment 7000B described above.

The data server 7010 is also configured to send electronic messages to the user computing devices 7050. The message may be in the form of an email, SMS message, automated voice message, or notification within the user program 7060.

The apparatus 4000 may be configured such that its treatment mode or settings for a particular treatment mode may be changed upon receipt of a corresponding command via its wide or local area network connection. In such an embodiment, the data server 7010 may also be configured to send such commands directly to the device 4000 (in embodiment 7000) or indirectly to the device 4000, relayed via the user computing device 7050 (in embodiment 7000B).

The data server 7010 carries a process 7020 that, as described in detail below, is configured to increase or maintain the motivation of the user to continue treatment or to continue other services. In broad terms, the process 7020 analyzes data from the user computing device 4000 and/or the user computing device 7050 to calculate a quality indicator that indicates the quality of the most recent treatment period or other type of user period as disclosed herein. The process 7020 then communicates the quality indicator to the user or patient 1000, for example, via a user program 7060 running on the user computing device 7050. The need to increase motivation may be detected by analyzing the low resolution data and high resolution data may be obtained to optimize the method of increasing or maintaining motivation of the patient.

Patient 1000 or the user perceives the treatment or other usage quality indicator as a concise indicator of how its treatment, exercise, or other period of time progresses. Thereby encouraging the user or patient 1000 to adhere to their treatment. It is known that tracking and measuring performance can be a strong motivation for humans to achieve their goals, and that therapeutic quality indicators are used as such performance measurements in the case of respiratory pressure therapy.

4.9.1 Predicting user compliance or Exit

Studies have shown that up to 90% of patients prescribed respiratory pressure treatment have at least some problems in meeting compliance rules. Examples of these problems include difficulty in setting up the RPT device 4000, discomfort due to a poorly fitted or poorly adjusted patient interface 3000, lack of tolerance to prescribed levels of positive airway pressure sensations, excessive leakage resulting in noise or interruption by the patient or the co-sleeper, and lack of improvement in subjective health. This results in low compliance, lower compensation and suboptimal health results for patient 1000 and higher overall long-term health care costs, as well as additional health care costs in the form of worsening related conditions for non-compliant patient 100. For example, in some countries, patients must meet a minimum level of compliance to be reimbursed. Accordingly, the inventors have developed techniques to predict whether patient 1000 will be compliant with therapy and to automatically intervene to improve compliance and ongoing therapy adherence and utilization.

For other systems and services, maintaining user engagement is also critical to their success, and identifying patients who may be out of use or out of service may be critical to intervening before they are out of or significantly less used.

In particular, the disclosed techniques and related apparatus 4000 may implement an automated system that monitors usage to identify the patient 1000 or other users that may be of reduced usage and automatically intervene (including by notifying the provider or patient 1000 or user). This provides the opportunity to adjust treatment, exercise or other related settings, switch to a more appropriate device, correct any problems with the patient 1000 or the user may have with the treatment or service, or provide other advice to help increase patient 1000 or user compliance and long term compliance results or service registration.

For example, it has been determined that trends in usage data can specifically predict future compliance and continuous usage. Thus, in some forms of the present technology, the usage data 9045 output from a device (e.g., RPT device 4000) may be monitored to determine when the patient 1000 or user may terminate treatment or reduce usage by a particular amount.

Fig. 9 illustrates an example of a process for predicting whether a patient or user will reduce or stop the use of the device 4000. First, the patient 1000 may begin a session or service (e.g., RPT therapy session, exercise session) by turning on the device 4000, using the device (e.g., wearing the device for a therapy session), and then turning off the device (e.g., and/or removing the mask once completed).

After completion of period 9000, device 4000 may output usage data 9010 to an external source, such as to a server and database over a network. In other examples, for example, the apparatus 4000 may store usage data 9045 locally and send the data to an external source after storing the usage data 9045 for one week. In a further example, apparatus 4000 may store data and process usage data locally on processor 4230 and memory 4360. The usage data 9045 may contain various types of information, including those disclosed above.

In some examples, demographic data, profile data, healthcare providers, machine types, and other data may be used in the model. This data may be stored locally on the device 4000 or separately in a database of reference patient IDs for efficient retrieval and updating of the algorithm. This data may not need to be updated each time the model or algorithm is updated and thus may be stored separately in some examples. This may also save bandwidth for transmitting usage data from the apparatus 4000.

Additionally, after outputting and storing the usage data 9045, the disclosed techniques may identify a time window of previously stored usage data 9020. For example, the disclosed technology may use algorithms to identify previously stored usage data 9020 recorded in the previous week, two weeks, three weeks, or other suitable time frame to identify usage trends.

Next, the disclosed techniques may process the data to determine the likelihood that the patient 1000 or user will reduce (or maintain) the usage level within the future time window 9030. For example, the disclosed techniques may determine a percentage likelihood that the patient 1000 or user will decrease from four hours to two hours in two weeks, or from three exercise periods to one exercise period in two weeks. In other examples, the disclosed techniques may determine a percentage likelihood that the patient 1000 or user will cease to be used within two weeks, three weeks, one week, or other predictive timeframes. In some examples, the disclosed techniques will process the data and predict the amount of device 4000, system, and/or service that the patient 1000 or user will use in a future time window (e.g., average hours per night) without having to predict whether the patient 1000 or user will reduce use.

The disclosed techniques may utilize various algorithms to determine the percentage of opportunities for the patient 1000 or user to reduce or stop using (or to maintain the current level of use) within a particular time window. The platform may utilize a variety of data sources including (1) usage data 9045 (including recent time period and historical usage data, as well as other types of data disclosed above), (2) demographic data 9035 (including age of patient), and (3) device type 9055 (including device type, model and manufacturer, including RPT device, exercise device, computing device, or other devices), as well as other suitable data. In some examples, the healthcare provider may also be input data. As described above, usage data 9045 may be output from device 4000, and other data sources may be stored on separate databases. In other examples, all data may be stored on the memory of apparatus 4000.

Next, the disclosed techniques may first calculate various features from data containing data from usage periods within a previous usage time window. For example, the disclosed techniques may determine patterns of non-usage days, average number of usage hours per week, age, engagement with an associated online platform, and other factors. In some examples, these features may be computed weekly to determine weekly trends for each of these features.

In some forms of the present technique, these features are then input into various algorithms to output a percentage likelihood that the patient 1000 will reduce use of a certain amount within a certain time window 9030. For example, the system may analyze the features using logistic regression, linear regression, and/or random forest algorithms. In some examples, where the output is a binary classification logistic regression, decision tree, random forest, bayesian network, support vector machine, neural network, or probabilistic model, may be used to output the probability that the input features result in the patient 1000 terminating the therapy. In other examples, machine learning algorithms and combinations of algorithms may be used to classify inputs into usage categories. This may include linear classifiers (logistic regression,Bayes classifier), support vector machine, decision tree, lifting tree, random forest, neural network, random gradient descent, nearest neighbors, etc.

In addition, other machine learning algorithms may be used. In some examples, a decision tree may be utilized to determine which pre-trained machine learning algorithm to apply. For example, different algorithms may be used depending on the age queue to which the patient 1000 or user belongs. In other examples, for example, different vendors or different types of devices 4000 may have different algorithms trained with data from these vendors. In other examples, the algorithm may output a binary determination of whether the patient 1000 or user is likely to be logged out, or whether the patient 1000 or user is likely to be appropriate for the amount of the range of use (e.g., 4-2 hours, 2-0 hours, or stopped).

Next, if the output percentage of the algorithm exceeds the threshold 9040, the disclosed techniques may output an indication that the patient 1000 or user may exit or be out of use within a certain time window. This can include marking the patient 1000 or user on an internal database of medical records, sending a notification to the display 4294 of the associated computing device, server or apparatus 4000. This would allow the healthcare provider to contact the patient 1000 or user or initiate various action steps discussed below.

4.9.2 Operating steps to perform interventions if the predicted usage is low

In some forms of the present technology, the notification may trigger an action to improve the treatment 9050 or reduce the likelihood that the patient 1000 or user will terminate or reduce the treatment or other service. For example, the patient 1000 or user terminal terminates or reduces its use for a number of reasons, including treatment adaptation challenges, problems related to equipment management, environmental factors, and motivational issues. The patient interface 3000 is not properly sized or fitted, (2) the patient 1000 is not used to wearing the RPT device 4000 while sleeping, (3) the patient 1000 has difficulty breathing forced air, (4) the leaky patient interface 3000 dries out the nose or mouth of the patient 1000, (5) excessive noise, (6) loud exercise equipment, (7) pain or pain, (8) or others.

Thus, the action steps will optimally address the reasons that the patient 1000 or user would like to reduce or terminate treatment or other services. Thus, the disclosed techniques may first apply various workflows or algorithms to determine why the patient 1000 or user may reduce or terminate use. For example, the disclosed techniques may send a notification or request to a display 4294 on the device 4000 that provides a menu or option and requests the patient 1000 or user to indicate which aspects of the therapy or other service the patient 1000 or user dislikes or does not have effectiveness.

The notification may also be text, email, pop-up notification on the mobile device, or other type of notification. In some examples, the frequency or content of notification may be changed to increase the incentive according to the probability of termination of the therapy or other service, or classification of use. In some examples, the menu may provide options for common questions that the patient 1000 or user may select. This may then provide the patient 1000 or user with further remedial options based on the patient 1000 or user's selections that would attempt to overcome the patient or user's difficulties with the treatment or other services disclosed herein. In some examples, this may include displaying video to the patient 1000, user, or other content to assist the patient 1000 or other user in using the device 4000 in situations where the patient 1000 or other user has difficulty using the device 4000.

In some examples, the current technology may use machine learning or other algorithms to estimate the reason why the patient 1000 or user may exit. This may involve monitoring a period of time or some aspect of the device 400 that is known to increase the chance that the patient 1000 or user may terminate treatment. In some examples, the determination that the patient 1000 or user may terminate treatment or reduce use will trigger analysis of other metrics that may be analyzed to determine the highest probability cause that the patient 1000 or user may exit. For example, leakage flow, noise, respiratory events, sleep scores, and other variables may be analyzed to determine which has the highest deviation from a normal, successful patient 1000 or user. The disclosed techniques may then ask the patient 1000 or user a question of the cause of the highest probability identified reduced service (e.g., respiratory therapy) and perform appropriate interventions as described in detail below. In some examples, the determination of likely causes may take into account demographic information (knowing that a particular age group is difficult to use with a device or its characteristics). The following is a list of action steps that may be taken, as well as monitoring or other algorithms that may automatically determine when to take these steps.

Altering service settings

In response to a prompt on the RPT device 4000 or an associated application or software program on the computing device, the patient or clinician 1000 or user may input a selection on the display or via the cloud management system 4294 indicating that there is a high leak from the patient interface, that the patient is bothersome to fall asleep or repeatedly wake up at night, that their sleep disordered breathing is not being effectively treated, or is experiencing other treatment-related problems resulting in ineffective treatment. Thus, the current technology may prompt the patient 1000 with an option to change the treatment settings 9065 on the RPT device 4000. Once those options are selected, the device receiving the patient input may send instructions to the controller of the RPT device 4000 to effect the change.

Tilt pressure while the patient is asleep

Some patients 1000 marked for reduced use may indicate that they are uncomfortable at high pressure and not easily falling asleep (e.g., in response to notifications having queries about their use). The current technology may then present the patient 1000 with the option of selecting a "RAMP" feature that implements a protocol on the RPT device 4000 to slowly increase the base pressure until the patient 1000 falls asleep at the beginning of the treatment.

In some examples, usage data 9045 may indicate that patient 1000 is removing the device within the first hour of turning on the device, or some other threshold that indicates difficulty in falling asleep for patient 1000. Thus, if the processing of the usage data 2045 indicates that the patient 1000 is out of use within a short time window, e.g., 30 minutes, or within an hour, the current technology may automatically suggest a RAMP feature. Additionally, if the processed usage data 9045 identifies a stop early in use (or, for example, a low number of hours of use per period), the current technology may adjust the ramp feature to have an even lower initial pressure to help the patient 1000 fall asleep.

Pressure, expiratory pressure relief and treatment mode adjustment

In some examples, patient 1000 may wake frequently, potentially indicating that the treatment settings are not optimal once patient 1000 is asleep. In this example, patient notification and input may be of lower value because patient 1000 may not be consciously aware of treatment settings while they are asleep. Thus, certain algorithms (as disclosed herein) may be utilized to identify impaired sleep, respiration, or other sleep problems, and automatically adjust treatment and respiratory comfort settings. This may also include other adjustments to the treatment pattern, including APAP versus CPAP, etc.

Bi-level positive airway pressure

In some examples, the current techniques may vary the inhalation pressure delivered to the patient 1000 if the patient 1000 indicates that they are uncomfortable with the inhalation pressure. In other examples, this may be provided as a pattern of attempts to the patient 1000, possibly if the patient 1000 does not know why their treatment is uncomfortable.

Humidification device

In some examples, patient 1000 may select an input indicating that their mouth or nose is too wet or too dry. Once the current technique receives this input from the patient 1000, it may automatically recommend increasing or decreasing the humidification level.

In other examples, if the patient 1000 provides an input that they have dry mouth or dry nose, the current techniques may automatically determine whether the leak flow rate QV exceeds a threshold that indicates that humidity should be increased. For example, once the current technology receives input that the patient 1000 indicates that they have a dry mouth, the current technology or RTP device 4000 may automatically query or initiate the leakage flow estimate 4316 to determine if it exceeds a threshold.

Patient interface fittings or device types

In response to a prompt on the RPT device 4000 or an associated application or software program on the computing device, the patient or clinician 1000 may enter a selection on the display 4294 indicating that their mouth is dry, the fit of the patient interface 3000 is not optimal (e.g., their face is damaged). For example, the patient 1000 may indicate that they feel a leak between the skin and the patient interface 3000, or other problems with the assembly of the device 9075, including as described herein. As described above, the leakage flow rate estimation 4316 module may determine that there is a problem with leakage through the patient interface 3000.

Thus, the current technology may prompt the patient 1000 to determine whether they feel that the fit of the patient interface 3000 is wrong, and in particular, whether they feel too small or too large. Accordingly, the current technology may then recommend replacement patient interface 3000 for patient 1000 based on the data profile and fit issues indicative of the current patient interface 3000 of patient 1000.

In some examples, the current technology may prompt the patient 1000 to indicate uncomfortable portions of the face in contact with the patent interface 3000. For example, the current technology may request that the patient 1000 click on a map of the face to indicate a location around the seal-forming structure 3100, the positioning and stabilizing structure 3300, or other portions of the patient interface 3000 that is not deformable. The platform may then recommend an alternative patient interface 3000 based on the uncomfortable position.

In some examples, patient interface 3000 may include sensors on positioning and stabilizing structure 3300 to determine how tight, e.g., the strap is. In some examples, this may involve a simple tension assessment or pressure between the strap and the head of the patient 1000. This information can be compared to the average tension and pressure to provide advice to the patient 1000 to relax or tighten the positioning and stabilizing structure 3300 or change dimensions.

Noise

Noise is a frequent cause of patient 1000 stopping treatment, including requests by his sleep partner. In response to a prompt on the RPT device 4000 or an associated application or software program on the computing device, the patient 1000 or clinician may enter a selection on the display or cloud software 4294 indicating that the noise is too loud or that their partner feels too loud. In this example, the current technology may perform a diagnostic check to determine if the RPT device 4000 itself and all of its channels and vents are generating more noise than a standard amount.

For example, current techniques may evaluate whether ambient noise is from RPT device 4000 deviating from an average or expected noise level.

Accordingly, the current technology and/or RPT device 4000 may contain acoustic sensors that sense ambient noise, noise associated with the RPT device 4000, and other forms of noise. In some cases, a machine learning algorithm may be used to classify the type of noise and identify and compare the level of noise associated with RPT device 4000 (or "expel" the noise and separate it from patient 1000 snoring and other environmental noise) to a standard value. If those values exceed a threshold, the current technology may recommend maintenance, resupply, or other remedial action of the component.

In other examples, if patient 1000 indicates that noise is a major issue, current techniques may automatically change treatment settings over a range and optimize the settings to reduce noise.

Treatment type

In response to prompts on the RPT device 4000 or associated applications or software programs on the computing device, the patient 1000 or clinician may enter selections on the display 4294 or cloud software indicating that they are generally not satisfied with the RPT device 4000 based treatment and rather have a different type of treatment 9085. In other examples, usage data 9045 may be so low that current techniques recommend different types of treatments, or patient 1000 may fail to participate in a treatment using RPT device 4000 after the current techniques take many different recommendations and actions. In this case, the current technology may recommend that the patient 1000 use additional or complementary forms of therapy. For example, the system may ask the patient 1000 if he or she would like a mandibular repositioning device or cognitive behavioral therapy.

4.9.3 Example 1 treatment termination predictor

The disclosed techniques have been used on anonymous datasets from patient data to test whether particular logistic regression and random forest algorithms can predict when the patient 1000 may accurately terminate treatment. It has been found that current techniques can utilize these algorithms to predict with 90% accuracy (on average) whether the patient 1000 will reduce or terminate the use of a respiratory therapy device (e.g., CPAP) within a two week time window. This was confirmed by testing the current technology in 20 different patient cohorts, each patient cohort being installed on a basis of 4,000-26,000. In one example, current technology identifies more than 40 patients per week as likely to terminate treatment from a group of about 16,000 patients.

Function set

In this example, usage data 9045 is first preprocessed to identify target features for processing by random forest and logistic regression algorithms. First, the current technology determines a time window and a prediction window of data to be considered. As shown in fig. 10, the current technology identifies a usage period that occurs three weeks before the current time/date. Each usage period data set may already contain some combination or arrangement of the following data information:

Date/time stamp;

start treatment time, stop treatment time;

total treatment time

Treatment and sensor data (e.g., identified respiratory events, treatment settings, etc.), and

Patient ID.

The data may be output from the RPT device 4000 (e.g., usage data and treatment data), and other portions of the data may be from a provider database. Next, each identified time period data is processed to identify usage characteristics that may be input into the algorithm. For example, the following table shows an example set of usage characteristics determined based on period data used in a study:

In addition, the features may include whether the user is registered in the patient participation or treatment management software platform (or app). These features are then extracted from the sample dataset and output 1000 as the following features for different patients.

The data is processed using a logistic regression algorithm that is trained using previous data with actual drop data (regardless of patient 1000) of the treatment actually discarded. Through model operation, studies revealed that some predictive features included (1) non-zero days of use (period) ("NZD"), (2) average non-zero use ("NZUse"), and (3) non-zero use standard deviation ("NZSD"). Thus, in some examples, a logistic regression algorithm may be developed that utilizes these three features or other combinations of features.

Processing algorithm

In this study, the trend of use based on these features was processed using a random forest and logistic regression algorithm to output the probability that each patient 1000 would terminate use within a period of two weeks. This involves checking the trend of the features on a weekly basis (features over the whole week to identify trends compared to subsequent weeks). Additionally, in this example, a logistic regression model is created for each day in the prediction window. Each of these models was trained using a three week window prior to the day of model prediction. Thus, for training data, for patient 1000 ending the treatment, a three week prior use data window ending (a) 1 day before termination, (b) 2 days before termination, etc., may be used to train the individual models, so that 14 models are trained in this case, since the termination prediction window is two weeks in this case.

The 14 models were next trained using data from patient 1000 who did not terminate treatment. These 14 models are trained with different time windows of usage data (as in the termination data) from the last day the patient 1000 was using the device (rather than the day of termination) until 14 days before the last day. Thus, after training 14 models with terminated and non-terminated patients, they can be used to predict the termination probability 1000 for the new patient.

Thus, in this case, 14 models are created so that they can be applied to the usage data and determine the probability of daily termination. Then, by combining probabilities for 14 day periods, the total probability of termination can be determined for each patient 1000 over a future two week window.

The following are examples of some raw data, the probability indicated for each patient 1000 (one anonymous patient per column) and whether they actually terminate ("actually decline") within two weeks:

The study provides evidence that the disclosed platform can predict treatment termination for patient 1000 with 88% -93% accuracy over a two week window. These surprising results would be extremely beneficial to healthcare providers so that they can intervene before patient 1000 ceases treatment. For example, once the patient has actually made a decision to terminate the treatment, intervention is more difficult. Thus, some advantages of this technique stem from the fact that termination is predicted rather than monitored or detected. The platform has the potential to predict future low compliance and termination, rather than low usage or compliance warnings. This will likely lead to much higher compliance and retention rates, as well as greatly improved results for patients 1000 suffering from sleep disorders.

4.9.4 Example 2 treatment compliance predictor

The disclosed techniques are also used with datasets from healthcare providers to test whether particular linear and logistic regression algorithms can predict whether a patient will remain compliant for a future time window. This is advantageous because in some countries the reimbursement depends on past compliance. For example, the reimbursement for a month in the future may depend on the compliance level meeting the threshold for the previous month (e.g., compliance for a past 28 day period may determine the reimbursement for a future 28 day period). In some countries, reimbursement may be based only on compliance after an initial ramp-up period (e.g., 10, 13, 14, or 15 weeks).

Thus, for some countries it may be advantageous to predict whether patient 1000 will be compliant with the next week, two weeks, three weeks, four weeks, eight weeks, or other time period according to local regulations. In this example, the disclosed techniques are used to determine whether compliance with a future 28-day cycle can be predicted based on past 28-day usage data. Specifically, the disclosed technique predicts whether the average usage of the patient 1000 over the next 28 day period is:

0-2 hours per day "[0,2 ]".

2-4 Hours per day "[0,4 ]".

4 Hours per day "[4,24 ]"'

The correlation of usage between consecutive 4 week intervals has been found to be very high (0.9). Thus, the average use of the previous four weeks can be used to estimate the use of the next four week interval. For example, the following equation is an example of how to determine:

Usage(t+1)=a+b*Usage(t)+error

Specifically, in this example, the following features were processed from the data to estimate compliance over 28 days:

(1) U1 average usage for the first three weeks of the first 28 day interval

(2) Average dosage of upper week of the first 28 day interval U2

(3) Non-zero days of use for the first 28 day interval

(4) Standard deviation of non-zero dose for the first 28 day interval sd_nz

(5) Interval of 28 days from the start of treatment

(6) Age of

These features are identified from various data sources as disclosed herein. Next, the features are processed in a first model using the following multiple linear regression model:

U=a+b*U1+c*U2+d*NOZERO_DAYS+e*SD_NZ+F*Age+g*Interval+error

in some examples, the interval feature may be discarded and the model should be trained for each interval (every 28 day period after start-up). The interval data may be obtained from the setting data output from the RTP apparatus 4000.

The model (or additional model) may be trained using data output from RPT device 4000, profile data, and other patient 1000 data. This contains the actual historical usage data and other data from the various 28 day intervals (in this example). The model may be trained by feeding training data from two consecutive 28-day intervals, for example, from real historical data.

In this example, the model performed well, but it was determined that accuracy could be improved between 28 day intervals for some categories:

Thus, a second model was developed to train a logistic regression algorithm to separate the data into [0.2] and [2,24] classes. The model may then be adapted to predict patients that fall into [0,2] and [2,4] from the first model and reclassify them with the second model. The second model uses a logistic regression equation with the same features as described above. Using this method increases the accuracy of the prediction and the following results are shown at 28 day intervals from week 3 to week 4:

this greatly improves the accuracy of the prediction of class 0, 2. Accordingly, continued improvements may be made to increase the accuracy of each category so that the disclosed techniques may reliably classify usage predictors of a patient with an accuracy approaching 90%.

The most important functions determined for compliance prediction include U1, U2, NOZERO _days, sd_nz, age and time interval. Less important features (at least in this model) include AHI (apnea-hypopnea index), peak (LEAK flow rate) and patient App index.

Example 3 exercise compliance predictor

The disclosed techniques may also be implemented in an exercise machine or other wearable appliance that measures exercise, sports, or other procedures that require a patient to adhere to or participate in. The hardware may include exercise equipment (e.g., a treadmill, stationary bicycle, or other exercise equipment) and a control system and processor as disclosed herein.

The system may monitor usage data as disclosed herein and provide notification as to when a user may exit or reduce their use of a device or digital service. For example, when usage data drops or it is determined that a trend in usage is identified, it may be determined that the user may be exiting exercise or engaged in a physical or other treatment plan. In some examples, the data may indicate that the user will reduce use of the wearable device.

Thus, appropriate interventions may be initiated, including notification to a user using the exercise equipment or other actuators disclosed herein. For example, a notification may be sent to the user's mobile device, or other intervention may be initiated. Various other interventions may be utilized, including notifications regarding applications associated with software programs that communicate with devices that include exercise equipment.

Various machine learning algorithms may be used to identify users that may be equivalent or reduce participation or compliance, including [ additional example algorithms ] random forests and logistic regression algorithms. This includes trends in exercise frequency, duration, intensity, and other suitable metrics.

Example 4 website usage/participation predictor

The disclosed techniques may also be implemented to monitor user interactions with a website or software program to predict participation or compliance with the service or program. The system may comprise a server, control system and user device as disclosed herein, and may additionally comprise a user interface comprising a touch screen, mouse, keyboard or other.

Thus, the system may monitor various usage data output from a user's interaction with a user interface of a particular software program, website, or other application. For example, usage data, which may be a monitor, may include (1) the amount of time spent browsing a particular site, (2) the use of a particular software program (e.g., CBT-based online therapy process), (3) the level of interaction with the user interface (mouse click, click link, engagement with various features), and (4) other aspects.

Various machine learning algorithms may be used to identify users that may be equivalent or reduce participation or compliance, including [ additional example algorithms ] random forests and logistic regression algorithms. This includes analyzing the usage trend and determining if the user is likely to reduce participation in or terminate their use of the website or software program.

Example 5 on-demand service/employment prediction

The disclosed techniques may also be implemented to monitor user interactions with a software program, such as on a mobile device, computer system, or other device, to determine whether they will likely terminate their employment or service based on employee or independent contractor interactions and the use of one or more user interfaces of the various devices.

For example, various on-demand services utilize employees or independent contractors' mobile phones to coordinate their services. These include on-demand taxi services, food delivery, and the like. Many employees or independent contractors may flexibly log into the service and be available for riding, delivery, or other services. Thus, the user's mobile phone and associated software may output usage data including frequency of logging in/making itself available, length of time period or service availability, number of service requests satisfied, customer rating, and other usage data.

Various machine learning algorithms may be utilized to identify users that may be equivalent to or reduce their employment or availability for services, including [ add example algorithms ] random forest and logistic regression algorithms. This includes analyzing the usage trend and determining whether the user is likely to reduce participation in or terminate their use of the website or software program associated with the service.

4.10 Glossary of terms

For purposes of this technical disclosure, one or more of the following definitions may be applied in certain forms of the present technology. In other forms of the present technology, alternative definitions may be applied.

4.10.1 General purpose

Air in some forms of the present technology, air may be considered to refer to atmospheric air, and in other forms of the present technology, air may be considered to refer to some other combination of breathable gases, such as atmospheric air enriched with oxygen.

Environment in certain forms of the present technology, the term "environment" refers to (i) being external to the treatment system or patient, and (ii) directly surrounding the treatment system or patient.

For example, the ambient humidity relative to the humidifier may be the humidity of the air immediately surrounding the humidifier, e.g. the humidity in a room in which the patient sleeps. Such ambient humidity may be different from the humidity outside the room in which the patient is sleeping.

In another example, the ambient pressure may be pressure directly around the body or outside the body.

In some forms, ambient (e.g., acoustic) noise may be considered to be the background noise level in the room in which the patient is located, in addition to noise generated by, for example, an RPT device or from a mask or patient interface. Ambient noise may be generated by sources outside the room.

Automatic Positive Airway Pressure (APAP) therapy-CPAP therapy, in which the therapeutic pressure is automatically adjustable, e.g., from one breath to another, between minimum and maximum, depending on the presence or absence of an SDB event indication.

Continuous Positive Airway Pressure (CPAP) therapy, which is respiratory pressure therapy in which the therapeutic pressure is approximately constant throughout the patient's respiratory cycle. In some forms, the pressure at the entrance to the airway will be slightly higher during exhalation and slightly lower during inhalation. In some forms, the pressure will vary between different respiratory cycles of the patient, for example, increasing in response to detecting an indication of partial upper airway obstruction, and decreasing in the absence of an indication of partial upper airway obstruction.

Flow rate: volume (or mass) of air delivered per unit time. Flow may refer to an instantaneous quantity. In some cases, the reference to flow will be a reference to a scalar, i.e., an amount having only a size. In other cases, the reference to flow will be a reference to a vector, i.e., a quantity having both magnitude and direction. Traffic may be given the symbol Q. The "flow" is sometimes shortened to a simple "flow" or "air stream".

In an example of patient breathing, the flow may be nominally positive for the inspiratory portion of the patient's breathing cycle and thus negative for the expiratory portion of the patient's breathing cycle. The device flow Qd is the flow of air leaving the RPT device. The total flow Qt is the flow of air and any supplemental gas to the patient interface via the air circuit. The ventilation flow Qv is the air flow leaving the ventilation port to allow flushing of the exhaled air. Leakage flow Q1 is leakage flow from the patient interface system or elsewhere. The respiratory flow Qr is the flow of air received into the respiratory system of the patient.

Humidifier the term humidifier will be taken to mean a humidification device constructed and arranged or configured with physical structures capable of providing a therapeutically beneficial amount of water (H2O) vapor to an air stream to improve the medical respiratory condition of a patient.

Leakage the term leakage refers to an undesired air flow. In one example, leakage may occur due to an incomplete seal between the mask and the patient's face. In another example, leakage may occur in a swivel elbow to the surrounding environment.

Noise, conductive (acoustic), conductive noise in this document refers to noise carried to the patient through pneumatic paths such as the air circuit and patient interface and air therein. In one form, the conducted noise may be quantified by measuring the sound pressure level at the end of the air circuit.

Noise, radiation (Acoustic: radiation noise in this document refers to noise carried by ambient air to a patient in one form, radiation noise may be quantified by measuring the acoustic power/pressure level of the object in question according to ISO 3744.

Noise, ventilated (acoustic): ventilation noise in this document refers to noise generated by air flow through any vent, such as a vent hole of a patient interface.

Patients, humans, whether or not they have respiratory disorders.

Pressure, force per unit area. The pressure can be measured in units of range, including cmH ₂O、g-f/cm² and hPa. 1cmH ₂ 0 is equal to 1g-f/cm ₂ and is about 0.98 hPa (1 hPa=100 Pa=100N/m ² =1 mbar-0.001 atm). In this specification, unless otherwise indicated, pressures are given in cmH ₂ O.

The pressure in the patient interface is given the symbol Pm, while the therapeutic pressure, which represents the target value achieved by the interface pressure Pm at the current moment, is given the symbol Pt.

Respiratory Pressure Therapy (RPT) is the application of an air supply to the inlet of the airway at a treatment pressure that is generally positive relative to the atmosphere.

Ventilator-a mechanical device that provides pressure support to a patient to perform some or all of the respiratory effort.

4.10.1.1 Material

Silicone or silicone elastomer, synthetic rubber. In the present specification, reference to silicone refers to Liquid Silicone Rubber (LSR) or Compression Molded Silicone Rubber (CMSR). One form of commercially available LSR is SILASTIC (included in the range of products sold under this trademark), manufactured by Dow corning corporation (Dow Coming). Another manufacturer of LSR is the Wacker group (Wacker). Unless specified to the contrary, exemplary forms of LSR have a shore a (or type a) indentation hardness ranging from about 35 to about 45 as measured using ASTM D2240.

Polycarbonate-transparent thermoplastic polymers of bisphenol A carbonate.

4.10.2 Respiratory cycle

Apneas an apnea is said to have occurred, according to some definitions, when the flow drops below a predetermined threshold value for a duration of, for example, 10 seconds. Obstructive apneas will be said to have occurred when some obstruction of the airway does not allow air flow despite patient effort. Central apneas are considered to occur when an apnea is detected due to a reduction in respiratory effort or the absence of respiratory effort, although the airway is open (patent). Mixed apneas are considered to occur when a reduction in respiratory effort or the absence of an airway obstruction occurs simultaneously.

Breathing rate-the spontaneous breathing rate of a patient is typically measured in breaths per minute.

Duty cycle: ratio of inhalation time Ti to total breath time Ttot.

Effort (breathing) the spontaneously breathing person tries to breathe the work done.

The expiratory portion of the respiratory cycle is the period of time from the start of the expiratory flow to the start of the inspiratory flow.

Flow restriction-flow restriction will be considered to be the case in the patient's breath where an increase in the patient's effort does not cause a corresponding increase in flow. Where the flow restriction occurs during the inspiratory portion of the respiratory cycle, it may be described as an inspiratory flow restriction. Where the flow restriction occurs during the expiratory portion of the respiratory cycle, it may be described as an expiratory flow restriction.

Flow limited inspiratory waveform type:

(i) Flat, having an ascending portion followed by a relatively flat portion followed by a descending portion.

(Ii) M-shape having two partial peaks, one at the leading edge and one at the trailing edge, and a relatively flat portion between the two peaks.

(Iii) Chair-shaped, having a single localized peak at the leading edge followed by a relatively flat portion.

(Iv) Inverted chair form, having a relatively flat portion followed by a single local peak, the peak being located at the trailing edge.

Hypopnea-by some definitions, hypopnea is considered to be a decrease in flow, rather than a cessation of flow. In one form, when the flow rate is below a threshold rate for a period of time, it can be said that hypopnea has occurred. When a hypopnea due to a reduced respiratory effort is detected, a central hypopnea will be considered to have occurred. In one form of adult, any of the following may be considered hypopneas:

(i) The patient breathing is reduced by 30% for at least 10 seconds plus the associated 4% desaturation, or

(Ii) The patient's respiration is reduced (but less than 50%) for at least 10 seconds with at least 3% associated desaturation or arousal.

Hyperbreathing-flow increases above normal.

The period of time from the start of inhalation flow to the start of exhalation flow will be considered the inhalation portion of the respiratory cycle.

Patency (airway) is the degree of airway opening, or the extent of airway opening. The patient's airway is open. Airway patency may be quantified, for example, with a value of one (1) being open and a value of zero (0) being closed (blocked).

Positive End Expiratory Pressure (PEEP), the pressure that exists above the atmosphere in the lungs at the end of expiration.

Peak flow (Qpeak) is the maximum value of flow during the inspiratory portion of the respiratory flow waveform.

Respiratory flow, patient flow, respiratory flow rate (Qr), these terms may be understood to refer to an estimate of the respiratory flow rate of the RPT device, as opposed to a "true respiratory flow rate" or "true respiratory flow rate," which is the actual respiratory flow rate experienced by the patient, typically expressed in liters/minute.

Tidal volume (Vt) the volume of air inhaled or exhaled during normal breathing when no additional effort is applied. In principle, the inspiratory volume Vi (the volume of inhaled air) is equal to the expiratory volume Ve (the volume of exhaled air), so a single tidal volume vt can be defined as being equal to either amount. In practice, the tidal volume vt is estimated as some combination, e.g. an average, of the inhalation volume Vi and the exhalation volume Ve.

Time (inspiration) Ti is the duration of the inspiratory portion of the respiratory flow waveform.

The (exhalation) time (Te) is the duration of the exhalation portion of the respiratory flow waveform.

The (total) time (Ttot) is the total duration between the start of one inspiratory portion of a respiratory flow waveform and the start of the next inspiratory portion of the respiratory flow waveform.

Typical recent ventilation is a measure of the concentration trend of recent values of ventilation Vent that tend to aggregate around them over some predetermined time scale.

Upper Airway Obstruction (UAO), including partial and total upper airway obstruction. This may be associated with a state of flow restriction, where the flow rate increases only slightly or may even decrease (Starling resistive behavior) when the pressure difference across the upper airway increases.

Ventilation (vent) is a measure of the rate at which gas is exchanged by the patient's respiratory system. The measurement of ventilation may involve one or both of inspiratory and expiratory flow per unit time. When expressed as a volume per minute, this amount is commonly referred to as "ventilation per minute". Ventilation per minute is sometimes expressed simply as volume, understood as volume per minute.

4.11 Other remarks

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the patent office patent document or the records, but reserves all copyright rights whatsoever.

Unless the context clearly indicates and provides a range of values, it is understood that every intermediate value between the upper and lower limits of the range, to one tenth of the unit of the lower limit, and any other such value or intermediate value within the range, is broadly encompassed within the technology. The upper and lower limits of these intermediate ranges may independently be included in the intermediate ranges, and are also encompassed within the technology, subject to any specifically excluded limit in the range. Where the range includes one or both of the limits, the art also includes ranges excluding either or both of those included limits.

Furthermore, where a value or values described herein are implemented as part of the technology, it is to be understood that such value or values may be approximate unless otherwise stated, and that such value or values may be used for any suitable significant digit to the extent that practical technical implementations may allow or require it.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present technology, a limited number of representative methods and materials are described herein.

Obvious replacement materials with similar properties are used as alternatives to particular materials when they are identified for use in configuring a component. Moreover, unless specified to the contrary, any and all components described herein are understood to be capable of being manufactured and thus may be manufactured together or separately.

It must be noted that, as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural equivalents thereof unless the context clearly dictates otherwise.

All publications mentioned herein are incorporated herein by reference in their entirety to disclose and describe the methods and/or materials which are the subject matter of those publications. The publications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the art is not entitled to antedate such publication by virtue of prior application. Furthermore, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

The terms "comprises" and "comprising" are to be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not referenced.

The topic headings used in the detailed description are for convenience only to the reader and should not be used to limit the topics that can be found throughout this disclosure or claims. The subject matter headings are not to be used to interpret the scope of the claims or limitations of the claims.

Although the technology has been described herein with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the technology. In some instances, the terms and symbols may imply specific details not required to practice the present technology. For example, although the terms "first" and "second" may be used, they are not intended to represent any order, unless otherwise indicated, but rather may be used to distinguish between different elements. Furthermore, although process steps in a method may be described or illustrated in a certain order, this order is not required. Those skilled in the art will recognize that this sequence may be modified and/or that aspects thereof may be performed simultaneously or even synchronously.

It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present technology.

Claims

1. A system for treating a respiratory disorder in a patient, the system comprising:

a respiratory therapy device configured to output usage data;

a memory including a machine-readable medium including machine-executable code having instructions stored thereon; and

A control system coupled to the memory, the control system comprising one or more processors, the control system configured to execute the machine executable code so that the control system:

receiving a usage data set for a treatment session of a patient from the respiratory therapy device;

identifying a previously stored usage data set for the patient within a past time window, the past time window being divided into a first duration and a second duration;

processing the usage data set and the previously stored usage data set with an algorithm to determine a likelihood that the patient will reduce usage of the respiratory therapy device within a future time window, the algorithm comprising a first regression model having corresponding coefficients for scaling corresponding inputs to the first regression model to determine the likelihood that the patient will reduce usage, the first regression model having a plurality of features as inputs, the features comprising an average number of hours of use of the device during the first duration and an average number of hours of use of the device during the second duration; and

If the likelihood is above a predetermined threshold, an indication is output that the patient may reduce usage.

2. The system of claim 1, wherein the usage data is output after each treatment session completed by the patient.

3. The system of claim 1, wherein the usage data includes total usage time for a period, and date and time stamp data.

4. The system of claim 1 , wherein the likelihood that the patient will reduce usage comprises a likelihood that the patient will terminate use of the respiratory therapy device.

5. The system of claim 1, wherein the likelihood that the patient will reduce usage comprises a likelihood that the patient will reduce usage to less than four hours per night.

6. The system of claim 1, wherein the likelihood that the patient will reduce usage comprises a likelihood that the patient will reduce usage to less than two hours per night.

7. The system of claim 1, wherein the algorithm pre-processes the usage data to determine non-usage days, average usage hours, and standard deviation of usage hours.

8. The system of claim 7, wherein the algorithm comprises a random forest or a logistic regression algorithm.

9. The system of claim 7, wherein the algorithm outputs a probability of abandonment for the patient.

10. The system of claim 7, wherein the algorithm further pre-processes the usage data to determine weekly trends in non-usage days, average usage hours, and standard deviation of usage hours.

11. The system of claim 7, wherein the algorithm is a collection of logistic regression models, each of which is trained using a separate previous time window of training data to estimate the probability of abandonment for each day in the future time window.

12. The system of claim 1, wherein the indication is an alarm, a notification on a patient mobile device, a notification on a provider computing device, or a notification on a display of the respiratory therapy device.

13. The system of claim 1 , wherein the further instructions include instructions to a display to depict selectable alternative therapies for the patient including at least RAMP therapy, and wherein each of the alternative therapies is associated with a set of therapy settings for the respiratory therapy device.

14. The system of claim 13, wherein the control system is further configured to:

receiving patient input including a selectable displayed selection of an alternative therapy by the patient; and

Instructions are sent to the respiratory therapy device to change the therapy setting of the respiratory therapy device based on the patient input.

15. The system of claim 1, wherein further instructions include instructions sent to a display to depict a selectable alternative patient interface to the patient.

16. The system of claim 15, wherein the control system is further configured to:

receiving patient input, the patient input comprising a selection by the patient of a selectable display of the alternate patient interface; and

Instructions are sent to a remote external device to purchase and deliver the patient's selection to the patient.

17. The system of claim 13, wherein the control system is further configured to:

receiving a second usage data set output from the respiratory therapy device;

identifying a second previously stored usage data set for the patient within a second past time window, the second past time window having a length equal to the length of the past time window;

processing the second usage data set and the second previously stored usage data set with the algorithm to determine a likelihood that the patient will reduce usage of the respiratory therapy device within a second future time window; and

18. The system of claim 13, wherein the control system is further configured to:

determining whether usage data has been received within a specific time window; and

If the control system determines that no usage data has been received within the specific time window, non-usage data is stored that references the specific time window.

19. The system of claim 18, wherein the specific time window is a 24-hour period.

20. The system of claim 19, wherein the non-usage data is a non-usage day.

21. The system of claim 1, wherein the algorithm is selected by the control system from a group of algorithms, each algorithm in the group of algorithms references a type of respiratory therapy device.

22. The system of claim 1, wherein the algorithm is selected by the control system from a group of algorithms based on a provider operating the respiratory therapy device.

23. The system of claim 1, wherein the respiratory therapy device is a CPAP.

24. The system of claim 1, wherein the algorithm is selected based on demographic information of the patient.

25. A method for predicting compliance, the method comprising:

receiving, at a control system including one or more processors, a usage data set for a patient output from a respiratory therapy device;

identifying, by the control system, a previously stored set of usage data for the patient within a past time window, the past time window being divided into a first duration and a second duration;

preprocessing, by the control system, the usage data set and the previously stored usage data set to output a usage feature set;

receiving, by the control system, profile data of the patient, the profile data including an age of the patient, a type of respiratory therapy device used by the patient, and a health care provider of the patient;

selecting, by the control system, an algorithm based on the profile data; and

processing, by the control system, the feature set with a selected algorithm to determine a likelihood that the patient will reduce usage of the respiratory therapy device within a future time window, the algorithm comprising a first regression model having corresponding coefficients for scaling corresponding inputs to the first regression model to determine the likelihood that the patient will reduce usage, the first regression model having as inputs a plurality of features, the features comprising an average number of hours of use of the device during the first duration and an average number of hours of use of the device during the second duration; and

26. The method of claim 25, wherein the algorithm is a set of models and each model in the set of models estimates the probability that the patient is likely to reduce use on a single day in the future time window, and each model in the set of models comprises a machine learning algorithm pre-trained using a training data set from patients with known termination outcomes and a previous time window beginning on the day that the model estimated the probability that the patient would reduce use of respiratory therapy.

27. The method of claim 25, wherein the algorithm is trained using data from a group of patients who terminated treatment and a group of patients who did not terminate treatment.

28. A non-transitory machine-readable medium having stored thereon instructions for performing a method comprising machine-executable code which, when executed by at least one machine, causes the machine to:

receiving, from a respiratory therapy device, a usage data set for a patient over a past time window, the past time window divided into a first duration and a second duration;

retrieving profile data of the patient from a database, the profile data including the age of the patient, the type of respiratory therapy device used by the patient, and the patient's health care provider;

processing the usage data set and the profile data with an algorithm to determine a likelihood that the patient will reduce usage of the respiratory therapy device within a future time window, the algorithm comprising a first regression model having corresponding coefficients for scaling corresponding inputs to the first regression model to determine the likelihood that the patient will reduce usage, the first regression model having a plurality of features as inputs, the features comprising an average number of hours of use of the device during the first duration and an average number of hours of use of the device during the second duration, and an age of the patient; and

29. The non-transitory machine-readable medium of claim 28, wherein the usage data comprises treatment settings.

30. The non-transitory machine-readable medium of claim 28, wherein the usage data comprises sensor readings output from the respiratory therapy device.

31. A non-transitory machine-readable medium having stored thereon instructions for performing a method comprising machine-executable code which, when executed by at least one machine, causes the machine to:

receiving a patient usage data set from a respiratory therapy device;

32. A system for predicting compliance, the system comprising:

a device configured to output usage data;

receiving from the device a usage data set for a time period of a user;

identifying a previously stored set of usage data for the user within a past time window, the past time window being divided into a first duration and a second duration;

processing the usage data set and the previously stored usage data set with an algorithm to determine a likelihood that the user will reduce usage of the device within a future time window, the algorithm comprising a first regression model having corresponding coefficients for scaling corresponding inputs to the first regression model to determine the likelihood that the patient will reduce usage, the first regression model having a plurality of features as inputs, the features comprising an average number of hours of use of the device during the first duration and an average number of hours of use of the device during the second duration; and

If the likelihood is above a predetermined threshold, an indication is output that the user is likely to reduce usage.

33. The system of claim 32, wherein the usage data is output each time a user completes a session.

34. The system of claim 32, wherein the usage data includes total usage time for a time period, and date and time stamp data.

35. The system of claim 32, wherein the likelihood that the user will reduce usage comprises a likelihood that the user will terminate use of the device.

36. The system of claim 32, wherein the likelihood that the user will reduce usage comprises a likelihood that the user will reduce usage below a threshold amount of time per week.

37. The system of claim 32, wherein the algorithm pre-processes the usage data to determine non-usage days, average usage hours, and standard deviation of usage hours.

38. The system of claim 32, wherein the algorithm comprises a random forest or a logistic regression algorithm.

39. The system of claim 32, wherein the algorithm outputs an abandonment probability for the user.

40. The system of claim 32, wherein the algorithm further pre-processes the usage data to determine weekly trends in non-usage days, average usage hours, and standard deviation of usage hours.

41. The system of claim 32, wherein the algorithm is a collection of logistic regression models, each of which is trained using a separate prior time window of training data to estimate the probability of abandonment for each day in the future time window.

42. The system of claim 32, wherein the indication is an alert notification on a user device.

43. The system of claim 32, wherein the device is an exercise machine.

44. The system of claim 32, wherein the device is a user interface for a software program.

45. The system of claim 32, wherein the usage data comprises intensity, length, or frequency.

46. A system for predicting compliance, the system comprising:

Receive the user's usage data set for the time period;